diff --git a/crop/SimData.ipynb b/crop/SimData.ipynb
index 3794a5b90067b71d1d0915f56787185c02c66379..b1727a7f94d9535275997777b4b527a7a116d792 100644
--- a/crop/SimData.ipynb
+++ b/crop/SimData.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -27,29 +27,85 @@
"#returns array of new images and coordinates\n",
"#TODO: make sure that the beetles and non-beetles don't overlap with each other\n",
"\n",
- "def generate_data(bg: list[Image], beetle_set: list[Image], num_beetles_arr: list[int]):\n",
+ "def generate_data(bg: list[Image], beetle_set: list[Image], num_beetles_arr: list[int], \n",
+ " non_beetle_set: list[Image], num_non_beetles_arr: list[int], overlap: bool):\n",
" set_size = len(beetle_set)\n",
+ " set_non_size = len(non_beetle_set)\n",
" set_bg_size = len(bg)\n",
" sim_arr, coords_arr = [],[]\n",
"\n",
- " for num_beetles in num_beetles_arr:\n",
+ " for i, num_beetles in enumerate(num_beetles_arr):\n",
+ " num_non_beetles = num_non_beetles_arr[i]\n",
" bg_id = np.random.randint(0, set_bg_size)\n",
" bg_temp = bg[bg_id].copy()\n",
" width, height = bg_temp.size\n",
" beetle_coords = []\n",
- " for _ in range(num_beetles):\n",
+ " non_beetle_coords = []\n",
+ " for j in range(num_non_beetles):\n",
+ " # get random non_beetle image\n",
+ " non_beetle_id = np.random.randint(0, set_non_size)\n",
+ " non_beetle_img = non_beetle_set[non_beetle_id]\n",
+ " \n",
+ " # get random non_beetle rotation\n",
+ " angle = np.random.randint(0, 360)\n",
+ " non_beetle_img = non_beetle_img.rotate(angle, resample=Image.BICUBIC, expand=1)\n",
+ " \n",
+ " non_beetle_width, non_beetle_height = non_beetle_img.size\n",
+ " \n",
+ " # get random x,y coords to paste non-beetle\n",
+ " x = np.random.randint(0, width - non_beetle_width)\n",
+ " y = np.random.randint(0, height - non_beetle_height)\n",
+ " \n",
+ " if not overlap:\n",
+ " is_overlapping = True\n",
+ " while (is_overlapping):\n",
+ " is_overlapping = False\n",
+ " for k in range(j):\n",
+ " _, x_, y_, width_, height_, _ = non_beetle_coords[-(k+1)]\n",
+ " if (x_ - width_/2) < x and (x_ + width_/2) > x and (y_ - height_/2) < y and (y_ + height_/2) > y:\n",
+ " is_overlapping = True\n",
+ " x = np.random.randint(0, width - non_beetle_width)\n",
+ " y = np.random.randint(0, height - non_beetle_height)\n",
+ " break\n",
+ "\n",
+ " bg_temp.paste(non_beetle_img, box=(x,y), mask=non_beetle_img)\n",
+ " \n",
+ " #centers x and y for YOLOv5 PyTorch label\n",
+ " x += non_beetle_width/2\n",
+ " y += non_beetle_width/2\n",
+ " non_beetle_coords.append((non_beetle_id, x, y, non_beetle_width, non_beetle_height, angle))\n",
+ "\n",
+ " \n",
+ " for j in range(num_beetles):\n",
" # get random beetle image\n",
" beetle_id = np.random.randint(0, set_size)\n",
" beetle_img = beetle_set[beetle_id]\n",
+ " \n",
+ " # get random beetle rotation\n",
+ " angle = np.random.randint(0, 360)\n",
+ " beetle_img = beetle_img.rotate(angle, resample=Image.BICUBIC, expand=1)\n",
+ " \n",
" beetle_width, beetle_height = beetle_img.size\n",
- "\n",
+ " \n",
" # get random x,y coords to paste beetle\n",
" x = np.random.randint(0, width - beetle_width)\n",
" y = np.random.randint(0, height - beetle_height)\n",
- "\n",
- " # get random beetle rotation (rotates about center)\n",
- " angle = np.random.randint(0, 360)\n",
- " beetle_img = beetle_img.rotate(angle, resample=Image.BICUBIC)\n",
+ " \n",
+ " if not overlap:\n",
+ " is_overlapping = True\n",
+ " while (is_overlapping):\n",
+ " is_overlapping = False\n",
+ " for k in range(j+num_non_beetles):\n",
+ " if (k < num_non_beetles):\n",
+ " _, x_, y_, width_, height_, _ = non_beetle_coords[-(k+1)]\n",
+ " else:\n",
+ " _, x_, y_, width_, height_, _ = beetle_coords[-(k+1-num_non_beetles)]\n",
+ " if (x_ - width_/2) < x and (x_ + width_/2) > x and (y_ - height_/2) < y and (y_ + height_/2) > y:\n",
+ " is_overlapping = True\n",
+ " x = np.random.randint(0, width - non_beetle_width)\n",
+ " y = np.random.randint(0, height - non_beetle_height)\n",
+ " break\n",
+ " \n",
"\n",
" bg_temp.paste(beetle_img, box=(x,y), mask=beetle_img)\n",
" \n",
@@ -65,7 +121,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -83,35 +139,48 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "bg = Image.open(\"imgs/bg.png\")\n",
+ "backgrounds = []\n",
+ "for file in glob.glob(r\"/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/*\"):\n",
+ " if \"bg.png\" in file: #clean trap\n",
+ " bg = Image.open(file)\n",
+ " # map corners of trap to corners of image\n",
+ " coeffs = find_coeffs([(128,6), (1904,62), (2113,3137), (3,3228)], \n",
+ " [(0,0), (bg.size[0], 0), (bg.size[0], bg.size[1]), (0, bg.size[1])])\n",
+ " bg_flat = bg.transform(bg.size, Image.PERSPECTIVE, coeffs, Image.BICUBIC)\n",
+ " backgrounds.append(bg)\n",
+ " continue\n",
+ " bg = Image.open(file)\n",
+ " backgrounds.append(bg);\n",
+ "\n",
"beetles = []\n",
"for file in glob.glob(r\"/raid/projects/akhot2/group-01-phys371-sp2023/crop/beetles/*\"):\n",
" b0 = Image.open(file)\n",
" beetles.append(b0);\n",
- "\n",
- "# map corners of trap to corners of image\n",
- "coeffs = find_coeffs([(128,6), (1904,62), (2113,3137), (3,3228)], \n",
- " [(0,0), (bg.size[0], 0), (bg.size[0], bg.size[1]), (0, bg.size[1])])\n",
- "bg_flat = bg.transform(bg.size, Image.PERSPECTIVE, coeffs, Image.BICUBIC)"
+ "non_beetles = []\n",
+ "for file in glob.glob(r\"/raid/projects/akhot2/group-01-phys371-sp2023/crop/non_beetles/*\"):\n",
+ " n_b0 = Image.open(file)\n",
+ " non_beetles.append(n_b0);"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "beetle_counts = np.random.randint(0, 6, size=1250)\n",
- "sim_img_arr, coords_arr = generate_data([bg_flat], beetles, beetle_counts)"
+ "beetle_counts = np.random.randint(0, 8, size=1250)\n",
+ "non_beetle_counts = np.random.randint(0, 6, size=1250)\n",
+ "overlap = True\n",
+ "sim_img_arr, coords_arr = generate_data(backgrounds, beetles, beetle_counts, non_beetles, non_beetle_counts, overlap)"
]
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -136,7 +205,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1400,6 +1469,46 @@
"export(sim_img_arr, coords_arr)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.4648"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "beetle_counts.mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.528"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "non_beetle_counts.mean()"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -1427,7 +1536,7 @@
},
{
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMIAAAD8CAYAAAAlkXvsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAKeklEQVR4nO3dT4xdZR3G8e9jKUgEI0UhBIlU0g0aU2pTMRCiiX9qN9WFCS60C5ISA4kuXFRJtC4h0YUhkpRAqIlCSJTIBrUhGDZGWrGWllLaKtFC08YYA25U6s/FeSe9aafl/pk5c+/M95O8OXfeOffOmZM+vWdOO++TqkJa6d611AcgTQODIGEQJMAgSIBBkACDIAFLEIQkm5McSXIsyY6+v740n/T57whJVgGvAp8FTgB7ga9U1cu9HYQ0j77fETYBx6rqz1X1H+AJYGvPxyCd55Kev971wN8GPj4BfOLcnZJsB7a3Dz/ew3FpGaqqDLtv30GY78DOuzarql3ALoAk/h8QLbq+L41OADcMfPxB4I2ej0E6T99B2AusS7I2yaXAncDTPR+DdJ5eL42q6u0k9wK/BlYBj1bVoT6PQZpPr7dPx+HPCBrXKD8s+y/LEgZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgARMGIclrSV5Ksj/Jvja3JsmeJEfb9qqB/b/dmnKOJPn8pAcvLZSFeEf4dFWtr6qN7eMdwLNVtQ54tn1MkpvpFv39CLAZ+HFr0JGW3GJcGm0FdrfHu4EvDsw/UVX/rqq/AMfoGnSkJTdpEAr4TZI/tJYbgGur6iRA217T5udry7l+vhdNsj3JvrnLLWmxTbos/G1V9UaSa4A9SV65yL5DteWAjTnq30TvCFX1RtueBp6iu9Q5leQ6gLY93Xa3LUdTa+wgJHlPkivnHgOfAw7SNeBsa7ttA37ZHj8N3JnksiRrgXXAC+N+fWkhTXJpdC3wVJK51/lZVf0qyV7gySR3AX8FvgxQVYeSPAm8DLwN3FNVZyY6emmB2JijZcvGHGlEBkHCIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkYIghJHk1yOsnBgbmRy0CSfLyVihxL8qO0JfKkaTDMO8JjdMUeg8YpA3kI2E635um6eV5TWjLvGISqeh74xznTI5WBtFWx31tVv6tujcmfDDxHWnLj/owwahnI9e3xufPSVJi0KORcFyoDGbokBLrGHLrLKKkX474jjFoGcqI9Pnd+XlW1q6o2DhQUSotq3CCMVAbSLp/eSnJru1v0tYHnSEuvqi46gMeBk8B/6f5mvwu4mu5u0dG2XTOw/33AceAI8IWB+Y10jTrHgQdp3QxDfP1yOMYZw/z5mhsWhWjZsihEGpFBkDAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBIzfmLMzyetJ9rexZeBzNuZo9gyx9ugdwAbg4MDcTuBb8+x7M/An4DJgLd06p6va514APkm3RPwzDKyL6tqnjsUYo6x9Om5jzoVsxcYczaBJfka4N8mBduk0Vya4II05SbYn2Zdk3wTHJw1t3CA8BNwErKdbMv4HbX5BGnMsClHfxgpCVZ2qqjNV9T/gYWBT+9SCNOZIfRsrCHO1Uc2X6ApAwMYczah3LBNM8jjwKeD9SU4A3wM+lWQ93eXNa8DdAFV1KMmTwMvA28A9VXWmvdTX6TqbL6e7a/TMAn4f0kRszNGyZWOONCKDIGEQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECRiuMeeGJM8lOZzkUJJvtPk1SfYkOdq2Vw08x9YczZYhGmuuAza0x1cCr9I14zwA7GjzO4D7F6M1hyloXnHM5ljoxpyTVfVie/wWcJiu5GMrsLvttpuzDThbsTVHM2aknxGS3AjcAvweuLYt907bXtN2m7g1x8Yc9e0dl4Wfk+QK4OfAN6vqzYtc3k/cmlNVu4Bd7evOu4+0kIZ6R0iymi4EP62qX7TpU3OFIW17us3bmqOZM8xdowCPAIer6ocDn3oa2NYeb+NsA46tOZo9Q9y1uZ3uEuYAsL+NLcDVwLPA0bZdM/Cc++juFh1h4M4QsJGuZuo48CCtqMS7Ro7FGKPcNbIxR8uWjTnSiAyChEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSMFlRyM4kryfZ38aWgedYFKLZMkFRyE7gW/Psb1GIYypGX0UhF7IVi0I0YyYpCgG4N8mBJI8OdKhZFKKZM3QQzi0KAR4CbgLWAyeBH8ztOs/T6yLz509W7aqqjVW1cdjjkyYxdlFIVZ2qqjNV9T/gYWBT292iEM2csYtC5tpymi/R9R6ARSGaQcN0qN0GfBV4Kcn+Nvcd4CtJ1tNd3rwG3A1QVYeSPAm8DLwN3FNVZ9rzvg48BlxOd9fomYX4JqRJWRSiZcuiEGlEBkHCIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBIw3Nqn707yQpI/tcac77f5NUn2JDnatlcNPMfGHM2WIRprAlzRHq+m60a4FXgA2NHmdwD325jjmKax0I05VVX/ah+ubqPomnF2t/ndnG2/2YqNOZoxw/YjrGorYZ8G9lTV74Fr21LvtO01bXcbczRzhgpCKwRZT1fusSnJRy+yu405mjkj3TWqqn8CvwU2A6fmykLa9nTbzcYczZxh7hp9IMn72uPLgc8Ar9A142xru23jbPuNjTmaPUPctfkY8EfgAF091Hfb/NXAs8DRtl0z8Jz76O4WHWHgzhCwsb3GceBBWlGJd40cizFGuWtkY46WLRtzpBEZBAmDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAuGSpD2AI/6L7P0uC9wN/X+qDmBLvdC4+NMqLzUIQjvh7CZ0k+zwXnYU+F14aSRgECZiNIOxa6gOYIp6Lsxb0XEz97yNIfZiFdwRp0RkEiSkOQpLNbcnIY0l2LPXxLIYkjyY5neTgwNyKXEozyQ1JnktyuC0t+o0238/5GOUXnPsawCq6X/D/MHAp3RKSNy/1cS3C93kHsAE4ODDXy1Ka0zaA64AN7fGVwKvte+7lfEzrO8Im4FhV/bmq/gM8QbeU5LJSVc8D/zhneisrcCnNqjpZVS+2x28Bh+lWQuzlfExrEC60bORKsGhLac6KJDcCt9AtON3L+ZjWIAy9POQKMvFSmrMgyRXAz4FvVtWbF9t1nrmxz8e0BuFCy0auBCt2Kc0kq+lC8NOq+kWb7uV8TGsQ9gLrkqxNcilwJ91SkivBilxKsx37I8DhqvrhwKf6OR9LfbfgIncRttDdOTgO3LfUx7NI3+PjwEngv3R/k91FT0tpTtsAbqe7hDkA7G9jS1/nw/9iITG9l0ZSrwyChEGQAIMgAQZBAgyCBBgECYD/A79FAQx8InyaAAAAAElFTkSuQmCC",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMIAAAD8CAYAAAAlkXvsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAKeklEQVR4nO3dT4xdZR3G8e9jKUgEI0UhBIlU0g0aU2pTMRCiiX9qN9WFCS60C5ISA4kuXFRJtC4h0YUhkpRAqIlCSJTIBrUhGDZGWrGWllLaKtFC08YYA25U6s/FeSe9aafl/pk5c+/M95O8OXfeOffOmZM+vWdOO++TqkJa6d611AcgTQODIGEQJMAgSIBBkACDIAFLEIQkm5McSXIsyY6+v740n/T57whJVgGvAp8FTgB7ga9U1cu9HYQ0j77fETYBx6rqz1X1H+AJYGvPxyCd55Kev971wN8GPj4BfOLcnZJsB7a3Dz/ew3FpGaqqDLtv30GY78DOuzarql3ALoAk/h8QLbq+L41OADcMfPxB4I2ej0E6T99B2AusS7I2yaXAncDTPR+DdJ5eL42q6u0k9wK/BlYBj1bVoT6PQZpPr7dPx+HPCBrXKD8s+y/LEgZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgARMGIclrSV5Ksj/Jvja3JsmeJEfb9qqB/b/dmnKOJPn8pAcvLZSFeEf4dFWtr6qN7eMdwLNVtQ54tn1MkpvpFv39CLAZ+HFr0JGW3GJcGm0FdrfHu4EvDsw/UVX/rqq/AMfoGnSkJTdpEAr4TZI/tJYbgGur6iRA217T5udry7l+vhdNsj3JvrnLLWmxTbos/G1V9UaSa4A9SV65yL5DteWAjTnq30TvCFX1RtueBp6iu9Q5leQ6gLY93Xa3LUdTa+wgJHlPkivnHgOfAw7SNeBsa7ttA37ZHj8N3JnksiRrgXXAC+N+fWkhTXJpdC3wVJK51/lZVf0qyV7gySR3AX8FvgxQVYeSPAm8DLwN3FNVZyY6emmB2JijZcvGHGlEBkHCIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkYIghJHk1yOsnBgbmRy0CSfLyVihxL8qO0JfKkaTDMO8JjdMUeg8YpA3kI2E635um6eV5TWjLvGISqeh74xznTI5WBtFWx31tVv6tujcmfDDxHWnLj/owwahnI9e3xufPSVJi0KORcFyoDGbokBLrGHLrLKKkX474jjFoGcqI9Pnd+XlW1q6o2DhQUSotq3CCMVAbSLp/eSnJru1v0tYHnSEuvqi46gMeBk8B/6f5mvwu4mu5u0dG2XTOw/33AceAI8IWB+Y10jTrHgQdp3QxDfP1yOMYZw/z5mhsWhWjZsihEGpFBkDAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBIzfmLMzyetJ9rexZeBzNuZo9gyx9ugdwAbg4MDcTuBb8+x7M/An4DJgLd06p6va514APkm3RPwzDKyL6tqnjsUYo6x9Om5jzoVsxcYczaBJfka4N8mBduk0Vya4II05SbYn2Zdk3wTHJw1t3CA8BNwErKdbMv4HbX5BGnMsClHfxgpCVZ2qqjNV9T/gYWBT+9SCNOZIfRsrCHO1Uc2X6ApAwMYczah3LBNM8jjwKeD9SU4A3wM+lWQ93eXNa8DdAFV1KMmTwMvA28A9VXWmvdTX6TqbL6e7a/TMAn4f0kRszNGyZWOONCKDIGEQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECRiuMeeGJM8lOZzkUJJvtPk1SfYkOdq2Vw08x9YczZYhGmuuAza0x1cCr9I14zwA7GjzO4D7F6M1hyloXnHM5ljoxpyTVfVie/wWcJiu5GMrsLvttpuzDThbsTVHM2aknxGS3AjcAvweuLYt907bXtN2m7g1x8Yc9e0dl4Wfk+QK4OfAN6vqzYtc3k/cmlNVu4Bd7evOu4+0kIZ6R0iymi4EP62qX7TpU3OFIW17us3bmqOZM8xdowCPAIer6ocDn3oa2NYeb+NsA46tOZo9Q9y1uZ3uEuYAsL+NLcDVwLPA0bZdM/Cc++juFh1h4M4QsJGuZuo48CCtqMS7Ro7FGKPcNbIxR8uWjTnSiAyChEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSMFlRyM4kryfZ38aWgedYFKLZMkFRyE7gW/Psb1GIYypGX0UhF7IVi0I0YyYpCgG4N8mBJI8OdKhZFKKZM3QQzi0KAR4CbgLWAyeBH8ztOs/T6yLz509W7aqqjVW1cdjjkyYxdlFIVZ2qqjNV9T/gYWBT292iEM2csYtC5tpymi/R9R6ARSGaQcN0qN0GfBV4Kcn+Nvcd4CtJ1tNd3rwG3A1QVYeSPAm8DLwN3FNVZ9rzvg48BlxOd9fomYX4JqRJWRSiZcuiEGlEBkHCIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBIw3Nqn707yQpI/tcac77f5NUn2JDnatlcNPMfGHM2WIRprAlzRHq+m60a4FXgA2NHmdwD325jjmKax0I05VVX/ah+ubqPomnF2t/ndnG2/2YqNOZoxw/YjrGorYZ8G9lTV74Fr21LvtO01bXcbczRzhgpCKwRZT1fusSnJRy+yu405mjkj3TWqqn8CvwU2A6fmykLa9nTbzcYczZxh7hp9IMn72uPLgc8Ar9A142xru23jbPuNjTmaPUPctfkY8EfgAF091Hfb/NXAs8DRtl0z8Jz76O4WHWHgzhCwsb3GceBBWlGJd40cizFGuWtkY46WLRtzpBEZBAmDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAuGSpD2AI/6L7P0uC9wN/X+qDmBLvdC4+NMqLzUIQjvh7CZ0k+zwXnYU+F14aSRgECZiNIOxa6gOYIp6Lsxb0XEz97yNIfZiFdwRp0RkEiSkOQpLNbcnIY0l2LPXxLIYkjyY5neTgwNyKXEozyQ1JnktyuC0t+o0238/5GOUXnPsawCq6X/D/MHAp3RKSNy/1cS3C93kHsAE4ODDXy1Ka0zaA64AN7fGVwKvte+7lfEzrO8Im4FhV/bmq/gM8QbeU5LJSVc8D/zhneisrcCnNqjpZVS+2x28Bh+lWQuzlfExrEC60bORKsGhLac6KJDcCt9AtON3L+ZjWIAy9POQKMvFSmrMgyRXAz4FvVtWbF9t1nrmxz8e0BuFCy0auBCt2Kc0kq+lC8NOq+kWb7uV8TGsQ9gLrkqxNcilwJ91SkivBilxKsx37I8DhqvrhwKf6OR9LfbfgIncRttDdOTgO3LfUx7NI3+PjwEngv3R/k91FT0tpTtsAbqe7hDkA7G9jS1/nw/9iITG9l0ZSrwyChEGQAIMgAQZBAgyCBBgECYD/A79FAQx8InyaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1439,7 +1548,7 @@
},
{
"data": {
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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMIAAAD8CAYAAAAlkXvsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAKeklEQVR4nO3dT4xdZR3G8e9jKUgEI0UhBIlU0g0aU2pTMRCiiX9qN9WFCS60C5ISA4kuXFRJtC4h0YUhkpRAqIlCSJTIBrUhGDZGWrGWllLaKtFC08YYA25U6s/FeSe9aafl/pk5c+/M95O8OXfeOffOmZM+vWdOO++TqkJa6d611AcgTQODIGEQJMAgSIBBkACDIAFLEIQkm5McSXIsyY6+v740n/T57whJVgGvAp8FTgB7ga9U1cu9HYQ0j77fETYBx6rqz1X1H+AJYGvPxyCd55Kev971wN8GPj4BfOLcnZJsB7a3Dz/ew3FpGaqqDLtv30GY78DOuzarql3ALoAk/h8QLbq+L41OADcMfPxB4I2ej0E6T99B2AusS7I2yaXAncDTPR+DdJ5eL42q6u0k9wK/BlYBj1bVoT6PQZpPr7dPx+HPCBrXKD8s+y/LEgZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgARMGIclrSV5Ksj/Jvja3JsmeJEfb9qqB/b/dmnKOJPn8pAcvLZSFeEf4dFWtr6qN7eMdwLNVtQ54tn1MkpvpFv39CLAZ+HFr0JGW3GJcGm0FdrfHu4EvDsw/UVX/rqq/AMfoGnSkJTdpEAr4TZI/tJYbgGur6iRA217T5udry7l+vhdNsj3JvrnLLWmxTbos/G1V9UaSa4A9SV65yL5DteWAjTnq30TvCFX1RtueBp6iu9Q5leQ6gLY93Xa3LUdTa+wgJHlPkivnHgOfAw7SNeBsa7ttA37ZHj8N3JnksiRrgXXAC+N+fWkhTXJpdC3wVJK51/lZVf0qyV7gySR3AX8FvgxQVYeSPAm8DLwN3FNVZyY6emmB2JijZcvGHGlEBkHCIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkYIghJHk1yOsnBgbmRy0CSfLyVihxL8qO0JfKkaTDMO8JjdMUeg8YpA3kI2E635um6eV5TWjLvGISqeh74xznTI5WBtFWx31tVv6tujcmfDDxHWnLj/owwahnI9e3xufPSVJi0KORcFyoDGbokBLrGHLrLKKkX474jjFoGcqI9Pnd+XlW1q6o2DhQUSotq3CCMVAbSLp/eSnJru1v0tYHnSEuvqi46gMeBk8B/6f5mvwu4mu5u0dG2XTOw/33AceAI8IWB+Y10jTrHgQdp3QxDfP1yOMYZw/z5mhsWhWjZsihEGpFBkDAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBIzfmLMzyetJ9rexZeBzNuZo9gyx9ugdwAbg4MDcTuBb8+x7M/An4DJgLd06p6va514APkm3RPwzDKyL6tqnjsUYo6x9Om5jzoVsxcYczaBJfka4N8mBduk0Vya4II05SbYn2Zdk3wTHJw1t3CA8BNwErKdbMv4HbX5BGnMsClHfxgpCVZ2qqjNV9T/gYWBT+9SCNOZIfRsrCHO1Uc2X6ApAwMYczah3LBNM8jjwKeD9SU4A3wM+lWQ93eXNa8DdAFV1KMmTwMvA28A9VXWmvdTX6TqbL6e7a/TMAn4f0kRszNGyZWOONCKDIGEQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECRiuMeeGJM8lOZzkUJJvtPk1SfYkOdq2Vw08x9YczZYhGmuuAza0x1cCr9I14zwA7GjzO4D7F6M1hyloXnHM5ljoxpyTVfVie/wWcJiu5GMrsLvttpuzDThbsTVHM2aknxGS3AjcAvweuLYt907bXtN2m7g1x8Yc9e0dl4Wfk+QK4OfAN6vqzYtc3k/cmlNVu4Bd7evOu4+0kIZ6R0iymi4EP62qX7TpU3OFIW17us3bmqOZM8xdowCPAIer6ocDn3oa2NYeb+NsA46tOZo9Q9y1uZ3uEuYAsL+NLcDVwLPA0bZdM/Cc++juFh1h4M4QsJGuZuo48CCtqMS7Ro7FGKPcNbIxR8uWjTnSiAyChEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAgyABBkECDIIEGAQJMAgSMFlRyM4kryfZ38aWgedYFKLZMkFRyE7gW/Psb1GIYypGX0UhF7IVi0I0YyYpCgG4N8mBJI8OdKhZFKKZM3QQzi0KAR4CbgLWAyeBH8ztOs/T6yLz509W7aqqjVW1cdjjkyYxdlFIVZ2qqjNV9T/gYWBT292iEM2csYtC5tpymi/R9R6ARSGaQcN0qN0GfBV4Kcn+Nvcd4CtJ1tNd3rwG3A1QVYeSPAm8DLwN3FNVZ9rzvg48BlxOd9fomYX4JqRJWRSiZcuiEGlEBkHCIEiAQZAAgyABBkECDIIEGAQJMAgSYBAkwCBIgEGQAIMgAQZBAgyCBBgECTAIEmAQJMAgSIBBkACDIAEGQQIMggQYBAkwCBIw3Nqn707yQpI/tcac77f5NUn2JDnatlcNPMfGHM2WIRprAlzRHq+m60a4FXgA2NHmdwD325jjmKax0I05VVX/ah+ubqPomnF2t/ndnG2/2YqNOZoxw/YjrGorYZ8G9lTV74Fr21LvtO01bXcbczRzhgpCKwRZT1fusSnJRy+yu405mjkj3TWqqn8CvwU2A6fmykLa9nTbzcYczZxh7hp9IMn72uPLgc8Ar9A142xru23jbPuNjTmaPUPctfkY8EfgAF091Hfb/NXAs8DRtl0z8Jz76O4WHWHgzhCwsb3GceBBWlGJd40cizFGuWtkY46WLRtzpBEZBAmDIAEGQQIMggQYBAkwCBJgECTAIEiAQZAAuGSpD2AI/6L7P0uC9wN/X+qDmBLvdC4+NMqLzUIQjvh7CZ0k+zwXnYU+F14aSRgECZiNIOxa6gOYIp6Lsxb0XEz97yNIfZiFdwRp0RkEiSkOQpLNbcnIY0l2LPXxLIYkjyY5neTgwNyKXEozyQ1JnktyuC0t+o0238/5GOUXnPsawCq6X/D/MHAp3RKSNy/1cS3C93kHsAE4ODDXy1Ka0zaA64AN7fGVwKvte+7lfEzrO8Im4FhV/bmq/gM8QbeU5LJSVc8D/zhneisrcCnNqjpZVS+2x28Bh+lWQuzlfExrEC60bORKsGhLac6KJDcCt9AtON3L+ZjWIAy9POQKMvFSmrMgyRXAz4FvVtWbF9t1nrmxz8e0BuFCy0auBCt2Kc0kq+lC8NOq+kWb7uV8TGsQ9gLrkqxNcilwJ91SkivBilxKsx37I8DhqvrhwKf6OR9LfbfgIncRttDdOTgO3LfUx7NI3+PjwEngv3R/k91FT0tpTtsAbqe7hDkA7G9jS1/nw/9iITG9l0ZSrwyChEGQAIMgAQZBAgyCBBgECYD/A79FAQx8InyaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1481,15 +1590,50 @@
"metadata": {},
"source": [
"First:\n",
- " - created 1000 images on a clean background variable 1 to 5 of the same beetle, model detected well on test set of 100 images (val/exp or exp2 or exp3) (proof of concept)\n",
- " - created 1000 images on a clean background variable 0 to 5 beetles of 6 different types of beetles, performing\n",
+ " - created 1250 images on a clean background variable 1 to 5 of the same beetle, model detected well on test set of 100 images (val/exp or exp2 or exp3) (proof of concept)\n",
+ " \n",
+ " fixed labels, making sure it's in the center\n",
+ " \n",
+ " - created 1250 images on a clean background variable 0 to 5 beetles of 6 different types of beetles, performing nearly 100%\n",
+ " https://wandb.ai/akhot2/YOLOv5/runs/294etqct?workspace=user-fresleven\n",
+ " https://wandb.ai/akhot2/YOLOv5/runs/i0ykd1a6?workspace=user-fresleven\n",
" \n",
"TODO:\n",
- " - created 1000 images on a clean background variable 0 to 5 beetles of 6 different images and 0 to 5 non-beetles of _ different images, performing\n",
- " - created 1000 images on a clean background variable 0 to 10 beetles of 15 different images and 0 to 10 non-beetles of _ different images\n",
- " - created 1000 images on a clean background variable 0 to 10 beetles of 15 different images and 0 to 10 non-beetles of _ different images on a dirty background\n",
+ " - (fixed autocropping during rotation and added option for not overlapping and multiple backgrounds and non beetles)\n",
+ " - created 1250 images on a clean background variable 0 to 7 beetles of 6 different images and 0 to 5 non-beetles 7 different images, performing \n",
+ " - created 1250 images on a clean and dirty background variables 0 to 15 beetles of 15 different images and 0 to 10 non-beetle of 10 different images, overlap True\n",
+ " \n",
+ " - created 1250 images on a clean and dirty background variables 0 to 10 beetles of 15 different images and 0 to 10 non-beetle of 10 different images, overlap False\n",
+ " \n",
" - perform auto cropping on arduino and include "
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(backgrounds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -1508,7 +1652,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.9.12"
},
"vscode": {
"interpreter": {
diff --git a/crop/autocrop_folder.py b/crop/autocrop_folder.py
index 2e51ed9c4252fd0b61f5e58a954a815ff7cb2cae..7a8091b21d1acbc5783c95272e17c793963890b7 100644
--- a/crop/autocrop_folder.py
+++ b/crop/autocrop_folder.py
@@ -110,7 +110,7 @@ for folder_name in glob.glob(sys.argv[1] + "*"):
crop(folder_name, sys.argv[2] + folder_name.split('/')[-1])
elif len(sys.argv) == 5:
crop(folder_name, sys.argv[2] + folder_name.split('/')[-1], sys.argv[3], sys.argv[4])
- elif type(sys.argv[3]) is int:
+ elif type(int(sys.argv[3])) is int:
crop(folder_name, sys.argv[2] + folder_name.split('/')[-1], sensitivity=sys.argv[3])
else:
crop(folder_name, sys.argv[2] + folder_name.split('/')[-1], debug=sys.argv[3])
\ No newline at end of file
diff --git a/yolov5_model/data/beetles_real.yaml b/yolov5_model/data/beetles_real.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dd7289cae87fd86b002f7ec063f90a97c6c8a666
--- /dev/null
+++ b/yolov5_model/data/beetles_real.yaml
@@ -0,0 +1,12 @@
+names:
+- beetles
+nc: 1
+roboflow:
+ license: CC BY 4.0
+ project: wcr-beetle-traps-dataset
+ url: https://universe.roboflow.com/wcr-beetle-detection/wcr-beetle-traps-dataset/dataset/5
+ version: 5
+ workspace: wcr-beetle-detection
+
+train: ../../crop/data/train/images
+val: ../../crop/real_data/images
diff --git a/yolov5_model/models/__pycache__/tf.cpython-39.pyc b/yolov5_model/models/__pycache__/tf.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9068348adb12d9e9b86e60e569a500660585cd3f
Binary files /dev/null and b/yolov5_model/models/__pycache__/tf.cpython-39.pyc differ
diff --git a/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best-fp16.tflite b/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best-fp16.tflite
new file mode 100644
index 0000000000000000000000000000000000000000..9bb9801ebdaa6c286115347477c7f89c5e1c2cd9
Binary files /dev/null and b/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best-fp16.tflite differ
diff --git a/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best_saved_model/saved_model.pb b/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best_saved_model/saved_model.pb
new file mode 100644
index 0000000000000000000000000000000000000000..7596672b4772b30be6bb0e004ff0fad61ddc29f4
Binary files /dev/null and b/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best_saved_model/saved_model.pb differ
diff --git a/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best_saved_model/variables/variables.data-00000-of-00001 b/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best_saved_model/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..27a449672b10917813a3e70414ca7e7542f83da6
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diff --git a/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best_saved_model/variables/variables.index b/yolov5_model/runs/train/6beetle_7-non_3dirt_bkg_over/weights/best_saved_model/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..b10c8f79d1a39bc2938e0403b6c1e86dda405980
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diff --git a/yolov5_model/runs/val/real_beetles_test/confusion_matrix.png b/yolov5_model/runs/val/real_beetles_test/confusion_matrix.png
new file mode 100644
index 0000000000000000000000000000000000000000..3bf69f1ab1c30cd4aec35a06d46817bcf19bcf1a
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diff --git a/yolov5_model/runs/val/real_beetles_test/val_batch0_labels.jpg b/yolov5_model/runs/val/real_beetles_test/val_batch0_labels.jpg
new file mode 100644
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diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/conda-environment.yaml
@@ -0,0 +1,332 @@
+name: akhot2
+channels:
+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
+dependencies:
+ - _libgcc_mutex=0.1=main
+ - _openmp_mutex=5.1=1_gnu
+ - _py-xgboost-mutex=2.0=cpu_0
+ - _tflow_select=2.3.0=mkl
+ - aiohttp=3.8.1=py39h7f8727e_1
+ - aiosignal=1.2.0=pyhd3eb1b0_0
+ - argon2-cffi=21.3.0=pyhd3eb1b0_0
+ - argon2-cffi-bindings=21.2.0=py39h7f8727e_0
+ - astor=0.8.1=py39h06a4308_0
+ - asttokens=2.0.5=pyhd3eb1b0_0
+ - astunparse=1.6.3=py_0
+ - async-timeout=4.0.1=pyhd3eb1b0_0
+ - atk-1.0=2.36.0=ha1a6a79_0
+ - attrs=21.4.0=pyhd3eb1b0_0
+ - awkward=0.15.0=pyhd3deb0d_0
+ - backcall=0.2.0=pyhd3eb1b0_0
+ - beautifulsoup4=4.11.1=py39h06a4308_0
+ - blas=1.0=mkl
+ - bleach=4.1.0=pyhd3eb1b0_0
+ - blinker=1.4=py39h06a4308_0
+ - blosc=1.21.0=h4ff587b_1
+ - bottleneck=1.3.4=py39hce1f21e_0
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+ - brotlipy=0.7.0=py39h27cfd23_1003
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+ - c-ares=1.18.1=h7f8727e_0
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+ - click=8.0.4=py39h06a4308_0
+ - cloudpickle=2.0.0=pyhd3eb1b0_0
+ - colorama=0.4.4=pyhd3eb1b0_0
+ - cramjam=2.5.0=py39h860a657_0
+ - cryptography=3.4.8=py39hd23ed53_0
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+ - detectron2=0.5=py39hf2442f4_1
+ - docker-pycreds=0.4.0=pyhd3eb1b0_0
+ - entrypoints=0.4=py39h06a4308_0
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+ - fontconfig=2.13.1=h6c09931_0
+ - fonts-anaconda=1=h8fa9717_0
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+ - freetype=2.11.0=h70c0345_0
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+ - icu=58.2=he6710b0_3
+ - importlib-metadata=4.11.3=py39h06a4308_0
+ - intel-openmp=2021.4.0=h06a4308_3561
+ - ipykernel=6.9.1=py39h06a4308_0
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+ - jupyter=1.0.0=py39h06a4308_7
+ - jupyter_client=7.2.2=py39h06a4308_0
+ - jupyter_console=6.4.3=pyhd3eb1b0_0
+ - jupyter_core=4.10.0=py39h06a4308_0
+ - jupyterlab_pygments=0.1.2=py_0
+ - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+ - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+ - kiwisolver=1.4.2=py39h295c915_0
+ - lame=3.100=h7b6447c_0
+ - lcms2=2.12=h3be6417_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgd=2.3.3=h695aa2c_1
+ - libgfortran-ng=7.5.0=ha8ba4b0_17
+ - libgfortran4=7.5.0=ha8ba4b0_17
+ - libgomp=11.2.0=h1234567_1
+ - libidn2=2.3.2=h7f8727e_0
+ - libllvm11=11.1.0=h3826bc1_1
+ - libopus=1.3.1=h7b6447c_0
+ - libpng=1.6.37=hbc83047_0
+ - libprotobuf=3.20.3=he621ea3_0
+ - librsvg=2.54.4=h19fe530_0
+ - libsodium=1.0.18=h7b6447c_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtasn1=4.16.0=h27cfd23_0
+ - libtiff=4.2.0=h2818925_1
+ - libtool=2.4.6=h295c915_1008
+ - libunistring=0.9.10=h27cfd23_0
+ - libuuid=1.0.3=h7f8727e_2
+ - libuv=1.40.0=h7b6447c_0
+ - libvpx=1.7.0=h439df22_0
+ - libwebp=1.2.2=h55f646e_0
+ - libwebp-base=1.2.2=h7f8727e_0
+ - libxcb=1.15=h7f8727e_0
+ - libxgboost=1.5.1=cpu_h3d145d1_2
+ - libxml2=2.9.14=h74e7548_0
+ - lime=0.2.0.1=pyh9f0ad1d_0
+ - lz4-c=1.9.3=h295c915_1
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+ - markdown=3.3.4=py39h06a4308_0
+ - markupsafe=2.1.1=py39h7f8727e_0
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+ - matplotlib-inline=0.1.2=pyhd3eb1b0_2
+ - mistune=0.8.4=py39h27cfd23_1000
+ - mkl=2021.4.0=h06a4308_640
+ - mkl-service=2.4.0=py39h7f8727e_0
+ - mkl_fft=1.3.1=py39hd3c417c_0
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+ - mock=4.0.3=pyhd3eb1b0_0
+ - multidict=5.2.0=py39h7f8727e_2
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+ - nest-asyncio=1.5.5=py39h06a4308_0
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+ - notebook=6.4.11=py39h06a4308_0
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+ - psutil=5.8.0=py39h27cfd23_1
+ - ptyprocess=0.7.0=pyhd3eb1b0_2
+ - pure_eval=0.2.2=pyhd3eb1b0_0
+ - py-xgboost=1.5.1=cpu_py39h4655687_2
+ - pyasn1=0.4.8=pyhd3eb1b0_0
+ - pyasn1-modules=0.2.8=py_0
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+ - python=3.9.12=h12debd9_1
+ - python-dateutil=2.8.2=pyhd3eb1b0_0
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+ - python_abi=3.9=2_cp39
+ - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0
+ - pytorch-model-summary=0.1.1=py_0
+ - pytorch-mutex=1.0=cuda
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+ - tensorboard-plugin-wit=1.6.0=py_0
+ - termcolor=1.1.0=py39h06a4308_1
+ - terminado=0.13.1=py39h06a4308_0
+ - testpath=0.5.0=pyhd3eb1b0_0
+ - threadpoolctl=2.2.0=pyh0d69192_0
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+ - torchaudio=0.11.0=py39_cu113
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+ - torchvision=0.12.0=py39_cu113
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+ - traitlets=5.1.1=pyhd3eb1b0_0
+ - typing_extensions=4.1.1=pyh06a4308_0
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+ - wandb=0.12.15=pyhd8ed1ab_0
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+ - webencodings=0.5.1=py39h06a4308_1
+ - werkzeug=2.0.3=pyhd3eb1b0_0
+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
+ - xz=5.2.5=h7f8727e_1
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+ - yaml=0.2.5=h7b6447c_0
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+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/config.yaml b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..73a11ccb4bca49da840805e5828e76eeefab96dc
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677453180
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 16
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
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+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp2
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 8
diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/output.log b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8e32f0665ab911a5e09eaf85780e153f5183a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/requirements.txt b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e113c026fbdf85a4d72907da3581363c7a7d333b
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/wandb-metadata.json
@@ -0,0 +1,35 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:13:26.684233",
+ "startedAt": "2023-02-26T23:13:00.043964",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "16",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..d6e023ab1c8294bfe311b3da5242f8acf46ce36a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171300-wj31wtse/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb": {"runtime": 26}}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_171300-wj31wtse/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..45a70621dcbc4e118be6790aef76cacc18275659
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171300-wj31wtse/logs/debug-internal.log
@@ -0,0 +1,135 @@
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diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/logs/debug.log b/yolov5_model/wandb/run-20230226_171300-wj31wtse/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..74eb4218d55741c9c62826fb2c875c9aa646044f
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diff --git a/yolov5_model/wandb/run-20230226_171300-wj31wtse/run-wj31wtse.wandb b/yolov5_model/wandb/run-20230226_171300-wj31wtse/run-wj31wtse.wandb
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index 0000000000000000000000000000000000000000..ce7a41edf8ea284844fd2cb5097f971091f8e9bf
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diff --git a/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
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+ - anaconda
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+ - fonts-anaconda=1=h8fa9717_0
+ - fonts-conda-ecosystem=1=hd3eb1b0_0
+ - fonttools=4.25.0=pyhd3eb1b0_0
+ - freetype=2.11.0=h70c0345_0
+ - fribidi=1.0.10=h7b6447c_0
+ - frozenlist=1.2.0=py39h7f8727e_0
+ - fsspec=2022.5.0=pyhd8ed1ab_0
+ - future=0.18.2=py39h06a4308_1
+ - fvcore=0.1.5.post20220506=pyhd8ed1ab_0
+ - gdk-pixbuf=2.42.8=h433bba3_0
+ - gdown=4.5.1=pyhd8ed1ab_0
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+ - gitpython=3.1.18=pyhd3eb1b0_1
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+ - gmp=6.2.1=h295c915_3
+ - gnutls=3.6.15=he1e5248_0
+ - gobject-introspection=1.68.0=py39he41a700_3
+ - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+ - google-pasta=0.2.0=pyhd3eb1b0_0
+ - graphite2=1.3.14=h295c915_1
+ - graphviz=2.50.0=h3cd0ef9_0
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+ - gstreamer=1.14.0=h28cd5cc_2
+ - gtk2=2.24.33=h73c1081_2
+ - gts=0.7.6=hb67d8dd_3
+ - h5py=3.6.0=py39ha0f2276_0
+ - harfbuzz=4.3.0=hd55b92a_0
+ - hdf5=1.10.6=hb1b8bf9_0
+ - icu=58.2=he6710b0_3
+ - importlib-metadata=4.11.3=py39h06a4308_0
+ - intel-openmp=2021.4.0=h06a4308_3561
+ - ipykernel=6.9.1=py39h06a4308_0
+ - ipython=8.3.0=py39h06a4308_0
+ - ipython_genutils=0.2.0=pyhd3eb1b0_1
+ - ipywidgets=7.6.5=pyhd3eb1b0_1
+ - jedi=0.18.1=py39h06a4308_1
+ - jinja2=3.0.3=pyhd3eb1b0_0
+ - joblib=1.1.0=pyhd3eb1b0_0
+ - jpeg=9e=h7f8727e_0
+ - jsonschema=4.4.0=py39h06a4308_0
+ - jupyter=1.0.0=py39h06a4308_7
+ - jupyter_client=7.2.2=py39h06a4308_0
+ - jupyter_console=6.4.3=pyhd3eb1b0_0
+ - jupyter_core=4.10.0=py39h06a4308_0
+ - jupyterlab_pygments=0.1.2=py_0
+ - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+ - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+ - kiwisolver=1.4.2=py39h295c915_0
+ - lame=3.100=h7b6447c_0
+ - lcms2=2.12=h3be6417_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgd=2.3.3=h695aa2c_1
+ - libgfortran-ng=7.5.0=ha8ba4b0_17
+ - libgfortran4=7.5.0=ha8ba4b0_17
+ - libgomp=11.2.0=h1234567_1
+ - libidn2=2.3.2=h7f8727e_0
+ - libllvm11=11.1.0=h3826bc1_1
+ - libopus=1.3.1=h7b6447c_0
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+ - libprotobuf=3.20.3=he621ea3_0
+ - librsvg=2.54.4=h19fe530_0
+ - libsodium=1.0.18=h7b6447c_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtasn1=4.16.0=h27cfd23_0
+ - libtiff=4.2.0=h2818925_1
+ - libtool=2.4.6=h295c915_1008
+ - libunistring=0.9.10=h27cfd23_0
+ - libuuid=1.0.3=h7f8727e_2
+ - libuv=1.40.0=h7b6447c_0
+ - libvpx=1.7.0=h439df22_0
+ - libwebp=1.2.2=h55f646e_0
+ - libwebp-base=1.2.2=h7f8727e_0
+ - libxcb=1.15=h7f8727e_0
+ - libxgboost=1.5.1=cpu_h3d145d1_2
+ - libxml2=2.9.14=h74e7548_0
+ - lime=0.2.0.1=pyh9f0ad1d_0
+ - lz4-c=1.9.3=h295c915_1
+ - lzo=2.10=h7b6447c_2
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+ - mkl=2021.4.0=h06a4308_640
+ - mkl-service=2.4.0=py39h7f8727e_0
+ - mkl_fft=1.3.1=py39hd3c417c_0
+ - mkl_random=1.2.2=py39h51133e4_0
+ - mock=4.0.3=pyhd3eb1b0_0
+ - multidict=5.2.0=py39h7f8727e_2
+ - munkres=1.1.4=py_0
+ - nbclient=0.5.13=py39h06a4308_0
+ - nbconvert=6.4.4=py39h06a4308_0
+ - nbformat=5.3.0=py39h06a4308_0
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+ - nest-asyncio=1.5.5=py39h06a4308_0
+ - nettle=3.7.3=hbbd107a_1
+ - ninja=1.10.2=h06a4308_5
+ - ninja-base=1.10.2=hd09550d_5
+ - notebook=6.4.11=py39h06a4308_0
+ - numexpr=2.8.1=py39h6abb31d_0
+ - numpy-base=1.21.5=py39ha15fc14_3
+ - oauthlib=3.2.0=pyhd3eb1b0_0
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+ - packaging=21.3=pyhd3eb1b0_0
+ - pandas=1.4.2=py39h295c915_0
+ - pandocfilters=1.5.0=pyhd3eb1b0_0
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+ - pathtools=0.1.2=pyhd3eb1b0_1
+ - pcre=8.45=h295c915_0
+ - pexpect=4.8.0=pyhd3eb1b0_3
+ - pickleshare=0.7.5=pyhd3eb1b0_1003
+ - pillow=9.0.1=py39h22f2fdc_0
+ - pip=21.2.4=py39h06a4308_0
+ - pixman=0.40.0=h7f8727e_1
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+ - prometheus_client=0.13.1=pyhd3eb1b0_0
+ - promise=2.3=py39h06a4308_0
+ - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+ - prompt_toolkit=3.0.20=hd3eb1b0_0
+ - psutil=5.8.0=py39h27cfd23_1
+ - ptyprocess=0.7.0=pyhd3eb1b0_2
+ - pure_eval=0.2.2=pyhd3eb1b0_0
+ - py-xgboost=1.5.1=cpu_py39h4655687_2
+ - pyasn1=0.4.8=pyhd3eb1b0_0
+ - pyasn1-modules=0.2.8=py_0
+ - pycocotools=2.0.2=py39hce5d2b2_2
+ - pycparser=2.21=pyhd3eb1b0_0
+ - pydot=1.4.2=py39hf3d152e_2
+ - pygments=2.11.2=pyhd3eb1b0_0
+ - pyjwt=2.1.0=py39h06a4308_0
+ - pyopenssl=21.0.0=pyhd3eb1b0_1
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+ - pysocks=1.7.1=py39h06a4308_0
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+ - pythia8=8.305=py39he80948d_0
+ - python=3.9.12=h12debd9_1
+ - python-dateutil=2.8.2=pyhd3eb1b0_0
+ - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
+ - python-graphviz=0.20.1=pyh22cad53_0
+ - python_abi=3.9=2_cp39
+ - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0
+ - pytorch-model-summary=0.1.1=py_0
+ - pytorch-mutex=1.0=cuda
+ - pytz=2022.1=py39h06a4308_0
+ - pyyaml=6.0=py39h7f8727e_1
+ - pyzmq=22.3.0=py39h295c915_2
+ - qt=5.9.7=h5867ecd_1
+ - qtconsole=5.3.0=pyhd3eb1b0_0
+ - qtpy=2.0.1=pyhd3eb1b0_0
+ - rclone=1.61.1=h519d9b9_0
+ - readline=8.1.2=h7f8727e_1
+ - requests=2.27.1=pyhd3eb1b0_0
+ - requests-oauthlib=1.3.0=py_0
+ - rsa=4.7.2=pyhd3eb1b0_1
+ - scikit-learn=1.0.2=py39h51133e4_1
+ - scipy=1.7.3=py39hc147768_0
+ - seaborn=0.11.2=pyhd3eb1b0_0
+ - send2trash=1.8.0=pyhd3eb1b0_1
+ - sentry-sdk=1.5.12=pyhd8ed1ab_0
+ - setproctitle=1.2.2=py39h27cfd23_1004
+ - shap=0.40.0=py39hde0f152_1
+ - shortuuid=1.0.8=py39hf3d152e_0
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+ - smart_open=5.2.1=py39h06a4308_0
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+ - soupsieve=2.3.1=pyhd3eb1b0_0
+ - sqlite=3.38.3=hc218d9a_0
+ - stack_data=0.2.0=pyhd3eb1b0_0
+ - tabulate=0.8.9=py39h06a4308_0
+ - tbb=2021.5.0=hd09550d_0
+ - tensorboard-plugin-wit=1.6.0=py_0
+ - termcolor=1.1.0=py39h06a4308_1
+ - terminado=0.13.1=py39h06a4308_0
+ - testpath=0.5.0=pyhd3eb1b0_0
+ - threadpoolctl=2.2.0=pyh0d69192_0
+ - tk=8.6.12=h1ccaba5_0
+ - torchaudio=0.11.0=py39_cu113
+ - torchinfo=1.6.5=pyhd8ed1ab_0
+ - torchvision=0.12.0=py39_cu113
+ - tornado=6.1=py39h27cfd23_0
+ - tqdm=4.64.0=py39h06a4308_0
+ - traitlets=5.1.1=pyhd3eb1b0_0
+ - typing_extensions=4.1.1=pyh06a4308_0
+ - tzdata=2022a=hda174b7_0
+ - uproot-methods=0.9.2=pyhd8ed1ab_0
+ - urllib3=1.26.9=py39h06a4308_0
+ - wandb=0.12.15=pyhd8ed1ab_0
+ - wcwidth=0.2.5=pyhd3eb1b0_0
+ - webencodings=0.5.1=py39h06a4308_1
+ - werkzeug=2.0.3=pyhd3eb1b0_0
+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
+ - xz=5.2.5=h7f8727e_1
+ - yacs=0.1.6=pyhd3eb1b0_1
+ - yaml=0.2.5=h7b6447c_0
+ - yarl=1.6.3=py39h27cfd23_0
+ - zeromq=4.3.4=h2531618_0
+ - zipp=3.8.0=py39h06a4308_0
+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/config.yaml b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6ac82bbad80d93a07358278be8aa5ea9bc13a0a2
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677453309
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 16
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp3
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 40
diff --git a/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/output.log b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8e32f0665ab911a5e09eaf85780e153f5183a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/requirements.txt b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..07be2aa40d91a40d16bff001c755666c02e90fc7
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:15:20.951971",
+ "startedAt": "2023-02-26T23:15:09.274122",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "16",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "40"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e32ca1c76b6fef6bdda575186a787cb42fc39532
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb": {"runtime": 13}}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171509-3v05xuqj/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..06e6bd36ea375b7c5c1d7fadb64255d7b6a47c58
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171509-3v05xuqj/logs/debug-internal.log
@@ -0,0 +1,151 @@
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diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/conda-environment.yaml
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index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
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+ - google-pasta=0.2.0=pyhd3eb1b0_0
+ - graphite2=1.3.14=h295c915_1
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+ - gstreamer=1.14.0=h28cd5cc_2
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+ - gts=0.7.6=hb67d8dd_3
+ - h5py=3.6.0=py39ha0f2276_0
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+ - hdf5=1.10.6=hb1b8bf9_0
+ - icu=58.2=he6710b0_3
+ - importlib-metadata=4.11.3=py39h06a4308_0
+ - intel-openmp=2021.4.0=h06a4308_3561
+ - ipykernel=6.9.1=py39h06a4308_0
+ - ipython=8.3.0=py39h06a4308_0
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+ - jupyter=1.0.0=py39h06a4308_7
+ - jupyter_client=7.2.2=py39h06a4308_0
+ - jupyter_console=6.4.3=pyhd3eb1b0_0
+ - jupyter_core=4.10.0=py39h06a4308_0
+ - jupyterlab_pygments=0.1.2=py_0
+ - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+ - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+ - kiwisolver=1.4.2=py39h295c915_0
+ - lame=3.100=h7b6447c_0
+ - lcms2=2.12=h3be6417_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgd=2.3.3=h695aa2c_1
+ - libgfortran-ng=7.5.0=ha8ba4b0_17
+ - libgfortran4=7.5.0=ha8ba4b0_17
+ - libgomp=11.2.0=h1234567_1
+ - libidn2=2.3.2=h7f8727e_0
+ - libllvm11=11.1.0=h3826bc1_1
+ - libopus=1.3.1=h7b6447c_0
+ - libpng=1.6.37=hbc83047_0
+ - libprotobuf=3.20.3=he621ea3_0
+ - librsvg=2.54.4=h19fe530_0
+ - libsodium=1.0.18=h7b6447c_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtasn1=4.16.0=h27cfd23_0
+ - libtiff=4.2.0=h2818925_1
+ - libtool=2.4.6=h295c915_1008
+ - libunistring=0.9.10=h27cfd23_0
+ - libuuid=1.0.3=h7f8727e_2
+ - libuv=1.40.0=h7b6447c_0
+ - libvpx=1.7.0=h439df22_0
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+ - libwebp-base=1.2.2=h7f8727e_0
+ - libxcb=1.15=h7f8727e_0
+ - libxgboost=1.5.1=cpu_h3d145d1_2
+ - libxml2=2.9.14=h74e7548_0
+ - lime=0.2.0.1=pyh9f0ad1d_0
+ - lz4-c=1.9.3=h295c915_1
+ - lzo=2.10=h7b6447c_2
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+ - markupsafe=2.1.1=py39h7f8727e_0
+ - matplotlib=3.5.1=py39h06a4308_1
+ - matplotlib-base=3.5.1=py39ha18d171_1
+ - matplotlib-inline=0.1.2=pyhd3eb1b0_2
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+ - mkl=2021.4.0=h06a4308_640
+ - mkl-service=2.4.0=py39h7f8727e_0
+ - mkl_fft=1.3.1=py39hd3c417c_0
+ - mkl_random=1.2.2=py39h51133e4_0
+ - mock=4.0.3=pyhd3eb1b0_0
+ - multidict=5.2.0=py39h7f8727e_2
+ - munkres=1.1.4=py_0
+ - nbclient=0.5.13=py39h06a4308_0
+ - nbconvert=6.4.4=py39h06a4308_0
+ - nbformat=5.3.0=py39h06a4308_0
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+ - nest-asyncio=1.5.5=py39h06a4308_0
+ - nettle=3.7.3=hbbd107a_1
+ - ninja=1.10.2=h06a4308_5
+ - ninja-base=1.10.2=hd09550d_5
+ - notebook=6.4.11=py39h06a4308_0
+ - numexpr=2.8.1=py39h6abb31d_0
+ - numpy-base=1.21.5=py39ha15fc14_3
+ - oauthlib=3.2.0=pyhd3eb1b0_0
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+ - packaging=21.3=pyhd3eb1b0_0
+ - pandas=1.4.2=py39h295c915_0
+ - pandocfilters=1.5.0=pyhd3eb1b0_0
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+ - pathtools=0.1.2=pyhd3eb1b0_1
+ - pcre=8.45=h295c915_0
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+ - pickleshare=0.7.5=pyhd3eb1b0_1003
+ - pillow=9.0.1=py39h22f2fdc_0
+ - pip=21.2.4=py39h06a4308_0
+ - pixman=0.40.0=h7f8727e_1
+ - portalocker=2.3.0=py39h06a4308_0
+ - prometheus_client=0.13.1=pyhd3eb1b0_0
+ - promise=2.3=py39h06a4308_0
+ - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+ - prompt_toolkit=3.0.20=hd3eb1b0_0
+ - psutil=5.8.0=py39h27cfd23_1
+ - ptyprocess=0.7.0=pyhd3eb1b0_2
+ - pure_eval=0.2.2=pyhd3eb1b0_0
+ - py-xgboost=1.5.1=cpu_py39h4655687_2
+ - pyasn1=0.4.8=pyhd3eb1b0_0
+ - pyasn1-modules=0.2.8=py_0
+ - pycocotools=2.0.2=py39hce5d2b2_2
+ - pycparser=2.21=pyhd3eb1b0_0
+ - pydot=1.4.2=py39hf3d152e_2
+ - pygments=2.11.2=pyhd3eb1b0_0
+ - pyjwt=2.1.0=py39h06a4308_0
+ - pyopenssl=21.0.0=pyhd3eb1b0_1
+ - pyqt=5.9.2=py39h2531618_6
+ - pyrsistent=0.18.0=py39heee7806_0
+ - pysocks=1.7.1=py39h06a4308_0
+ - pytables=3.6.1=py39h77479fe_1
+ - pythia8=8.305=py39he80948d_0
+ - python=3.9.12=h12debd9_1
+ - python-dateutil=2.8.2=pyhd3eb1b0_0
+ - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
+ - python-graphviz=0.20.1=pyh22cad53_0
+ - python_abi=3.9=2_cp39
+ - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0
+ - pytorch-model-summary=0.1.1=py_0
+ - pytorch-mutex=1.0=cuda
+ - pytz=2022.1=py39h06a4308_0
+ - pyyaml=6.0=py39h7f8727e_1
+ - pyzmq=22.3.0=py39h295c915_2
+ - qt=5.9.7=h5867ecd_1
+ - qtconsole=5.3.0=pyhd3eb1b0_0
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+ - rclone=1.61.1=h519d9b9_0
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+ - requests=2.27.1=pyhd3eb1b0_0
+ - requests-oauthlib=1.3.0=py_0
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+ - scipy=1.7.3=py39hc147768_0
+ - seaborn=0.11.2=pyhd3eb1b0_0
+ - send2trash=1.8.0=pyhd3eb1b0_1
+ - sentry-sdk=1.5.12=pyhd8ed1ab_0
+ - setproctitle=1.2.2=py39h27cfd23_1004
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+ - soupsieve=2.3.1=pyhd3eb1b0_0
+ - sqlite=3.38.3=hc218d9a_0
+ - stack_data=0.2.0=pyhd3eb1b0_0
+ - tabulate=0.8.9=py39h06a4308_0
+ - tbb=2021.5.0=hd09550d_0
+ - tensorboard-plugin-wit=1.6.0=py_0
+ - termcolor=1.1.0=py39h06a4308_1
+ - terminado=0.13.1=py39h06a4308_0
+ - testpath=0.5.0=pyhd3eb1b0_0
+ - threadpoolctl=2.2.0=pyh0d69192_0
+ - tk=8.6.12=h1ccaba5_0
+ - torchaudio=0.11.0=py39_cu113
+ - torchinfo=1.6.5=pyhd8ed1ab_0
+ - torchvision=0.12.0=py39_cu113
+ - tornado=6.1=py39h27cfd23_0
+ - tqdm=4.64.0=py39h06a4308_0
+ - traitlets=5.1.1=pyhd3eb1b0_0
+ - typing_extensions=4.1.1=pyh06a4308_0
+ - tzdata=2022a=hda174b7_0
+ - uproot-methods=0.9.2=pyhd8ed1ab_0
+ - urllib3=1.26.9=py39h06a4308_0
+ - wandb=0.12.15=pyhd8ed1ab_0
+ - wcwidth=0.2.5=pyhd3eb1b0_0
+ - webencodings=0.5.1=py39h06a4308_1
+ - werkzeug=2.0.3=pyhd3eb1b0_0
+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
+ - xz=5.2.5=h7f8727e_1
+ - yacs=0.1.6=pyhd3eb1b0_1
+ - yaml=0.2.5=h7b6447c_0
+ - yarl=1.6.3=py39h27cfd23_0
+ - zeromq=4.3.4=h2531618_0
+ - zipp=3.8.0=py39h06a4308_0
+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/config.yaml b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0615250f441cc6710262b070b87976c4a3271ea5
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677453456
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 16
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp4
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 30
diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/output.log b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8e32f0665ab911a5e09eaf85780e153f5183a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/requirements.txt b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..be8afabaab20ed19a549bacff02457cf37c92aaa
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:17:55.958683",
+ "startedAt": "2023-02-26T23:17:36.610904",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "16",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "30"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..c3ad6a3530e72181b3b2e93339791388b04999cd
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb": {"runtime": 21}}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..1e2e74f3863afa515b2e46de8fb32f3cf6987737
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/logs/debug-internal.log
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diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/logs/debug.log b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/logs/debug.log
new file mode 100644
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--- /dev/null
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diff --git a/yolov5_model/wandb/run-20230226_171736-3oka0g8j/run-3oka0g8j.wandb b/yolov5_model/wandb/run-20230226_171736-3oka0g8j/run-3oka0g8j.wandb
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index 0000000000000000000000000000000000000000..c82dba3dc5ee0d5e68ac603a846b960da89e86d7
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diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/conda-environment.yaml
@@ -0,0 +1,332 @@
+name: akhot2
+channels:
+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
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+ - _libgcc_mutex=0.1=main
+ - _openmp_mutex=5.1=1_gnu
+ - _py-xgboost-mutex=2.0=cpu_0
+ - _tflow_select=2.3.0=mkl
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+ - aiosignal=1.2.0=pyhd3eb1b0_0
+ - argon2-cffi=21.3.0=pyhd3eb1b0_0
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+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
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+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/files/config.yaml b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..330b3bae8a321671fa841cdd6696669116f092d9
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677453528
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
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+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 8
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
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+ hsv_h: 0.015
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+ momentum: 0.937
+ mosaic: 1.0
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+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
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+ value: false
+noval:
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+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp5
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 30
diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/files/output.log b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8e32f0665ab911a5e09eaf85780e153f5183a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/files/requirements.txt b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b9943c5849a896d889cf1a96f83fcd5946e4c9e0
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:18:58.190526",
+ "startedAt": "2023-02-26T23:18:48.307158",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "8",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "30"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e776ad5e9dd16ad8bbb479aaf2ef49394318a964
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171848-1806ognd/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb": {"runtime": 23}}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_171848-1806ognd/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..028c2d1f78b3e6e1f03726d230f2b818ae5f4954
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_171848-1806ognd/logs/debug-internal.log
@@ -0,0 +1,155 @@
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diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/logs/debug.log b/yolov5_model/wandb/run-20230226_171848-1806ognd/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..b6262f6f1c1e027c2ac5db8361de1b8de214c531
--- /dev/null
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diff --git a/yolov5_model/wandb/run-20230226_171848-1806ognd/run-1806ognd.wandb b/yolov5_model/wandb/run-20230226_171848-1806ognd/run-1806ognd.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..8a47eab32d4a1c1f814f4b3c691a9ef98219af23
Binary files /dev/null and b/yolov5_model/wandb/run-20230226_171848-1806ognd/run-1806ognd.wandb differ
diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/conda-environment.yaml
@@ -0,0 +1,332 @@
+name: akhot2
+channels:
+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
+dependencies:
+ - _libgcc_mutex=0.1=main
+ - _openmp_mutex=5.1=1_gnu
+ - _py-xgboost-mutex=2.0=cpu_0
+ - _tflow_select=2.3.0=mkl
+ - aiohttp=3.8.1=py39h7f8727e_1
+ - aiosignal=1.2.0=pyhd3eb1b0_0
+ - argon2-cffi=21.3.0=pyhd3eb1b0_0
+ - argon2-cffi-bindings=21.2.0=py39h7f8727e_0
+ - astor=0.8.1=py39h06a4308_0
+ - asttokens=2.0.5=pyhd3eb1b0_0
+ - astunparse=1.6.3=py_0
+ - async-timeout=4.0.1=pyhd3eb1b0_0
+ - atk-1.0=2.36.0=ha1a6a79_0
+ - attrs=21.4.0=pyhd3eb1b0_0
+ - awkward=0.15.0=pyhd3deb0d_0
+ - backcall=0.2.0=pyhd3eb1b0_0
+ - beautifulsoup4=4.11.1=py39h06a4308_0
+ - blas=1.0=mkl
+ - bleach=4.1.0=pyhd3eb1b0_0
+ - blinker=1.4=py39h06a4308_0
+ - blosc=1.21.0=h4ff587b_1
+ - bottleneck=1.3.4=py39hce1f21e_0
+ - brotli=1.0.9=he6710b0_2
+ - brotlipy=0.7.0=py39h27cfd23_1003
+ - bzip2=1.0.8=h7b6447c_0
+ - c-ares=1.18.1=h7f8727e_0
+ - ca-certificates=2023.01.10=h06a4308_0
+ - cachetools=4.2.2=pyhd3eb1b0_0
+ - cairo=1.16.0=h19f5f5c_2
+ - certifi=2022.12.7=pyhd8ed1ab_0
+ - cffi=1.15.0=py39hd667e15_1
+ - charset-normalizer=2.0.4=pyhd3eb1b0_0
+ - click=8.0.4=py39h06a4308_0
+ - cloudpickle=2.0.0=pyhd3eb1b0_0
+ - colorama=0.4.4=pyhd3eb1b0_0
+ - cramjam=2.5.0=py39h860a657_0
+ - cryptography=3.4.8=py39hd23ed53_0
+ - cudatoolkit=11.3.1=h2bc3f7f_2
+ - cython=0.29.28=py39h295c915_0
+ - dataclasses=0.8=pyh6d0b6a4_7
+ - dbus=1.13.18=hb2f20db_0
+ - debugpy=1.5.1=py39h295c915_0
+ - decorator=5.1.1=pyhd3eb1b0_0
+ - defusedxml=0.7.1=pyhd3eb1b0_0
+ - detectron2=0.5=py39hf2442f4_1
+ - docker-pycreds=0.4.0=pyhd3eb1b0_0
+ - entrypoints=0.4=py39h06a4308_0
+ - et_xmlfile=1.1.0=py39h06a4308_0
+ - executing=0.8.3=pyhd3eb1b0_0
+ - expat=2.4.4=h295c915_0
+ - fastparquet=0.8.1=py39hd257fcd_0
+ - ffmpeg=4.2.2=h20bf706_0
+ - filelock=3.7.1=pyhd8ed1ab_0
+ - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
+ - font-ttf-inconsolata=2.001=hcb22688_0
+ - font-ttf-source-code-pro=2.030=hd3eb1b0_0
+ - font-ttf-ubuntu=0.83=h8b1ccd4_0
+ - fontconfig=2.13.1=h6c09931_0
+ - fonts-anaconda=1=h8fa9717_0
+ - fonts-conda-ecosystem=1=hd3eb1b0_0
+ - fonttools=4.25.0=pyhd3eb1b0_0
+ - freetype=2.11.0=h70c0345_0
+ - fribidi=1.0.10=h7b6447c_0
+ - frozenlist=1.2.0=py39h7f8727e_0
+ - fsspec=2022.5.0=pyhd8ed1ab_0
+ - future=0.18.2=py39h06a4308_1
+ - fvcore=0.1.5.post20220506=pyhd8ed1ab_0
+ - gdk-pixbuf=2.42.8=h433bba3_0
+ - gdown=4.5.1=pyhd8ed1ab_0
+ - gensim=4.1.2=py39h295c915_0
+ - giflib=5.2.1=h7b6447c_0
+ - gitdb=4.0.7=pyhd3eb1b0_0
+ - gitpython=3.1.18=pyhd3eb1b0_1
+ - glib=2.69.1=h4ff587b_1
+ - gmp=6.2.1=h295c915_3
+ - gnutls=3.6.15=he1e5248_0
+ - gobject-introspection=1.68.0=py39he41a700_3
+ - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+ - google-pasta=0.2.0=pyhd3eb1b0_0
+ - graphite2=1.3.14=h295c915_1
+ - graphviz=2.50.0=h3cd0ef9_0
+ - gst-plugins-base=1.14.0=h8213a91_2
+ - gstreamer=1.14.0=h28cd5cc_2
+ - gtk2=2.24.33=h73c1081_2
+ - gts=0.7.6=hb67d8dd_3
+ - h5py=3.6.0=py39ha0f2276_0
+ - harfbuzz=4.3.0=hd55b92a_0
+ - hdf5=1.10.6=hb1b8bf9_0
+ - icu=58.2=he6710b0_3
+ - importlib-metadata=4.11.3=py39h06a4308_0
+ - intel-openmp=2021.4.0=h06a4308_3561
+ - ipykernel=6.9.1=py39h06a4308_0
+ - ipython=8.3.0=py39h06a4308_0
+ - ipython_genutils=0.2.0=pyhd3eb1b0_1
+ - ipywidgets=7.6.5=pyhd3eb1b0_1
+ - jedi=0.18.1=py39h06a4308_1
+ - jinja2=3.0.3=pyhd3eb1b0_0
+ - joblib=1.1.0=pyhd3eb1b0_0
+ - jpeg=9e=h7f8727e_0
+ - jsonschema=4.4.0=py39h06a4308_0
+ - jupyter=1.0.0=py39h06a4308_7
+ - jupyter_client=7.2.2=py39h06a4308_0
+ - jupyter_console=6.4.3=pyhd3eb1b0_0
+ - jupyter_core=4.10.0=py39h06a4308_0
+ - jupyterlab_pygments=0.1.2=py_0
+ - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+ - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+ - kiwisolver=1.4.2=py39h295c915_0
+ - lame=3.100=h7b6447c_0
+ - lcms2=2.12=h3be6417_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgd=2.3.3=h695aa2c_1
+ - libgfortran-ng=7.5.0=ha8ba4b0_17
+ - libgfortran4=7.5.0=ha8ba4b0_17
+ - libgomp=11.2.0=h1234567_1
+ - libidn2=2.3.2=h7f8727e_0
+ - libllvm11=11.1.0=h3826bc1_1
+ - libopus=1.3.1=h7b6447c_0
+ - libpng=1.6.37=hbc83047_0
+ - libprotobuf=3.20.3=he621ea3_0
+ - librsvg=2.54.4=h19fe530_0
+ - libsodium=1.0.18=h7b6447c_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtasn1=4.16.0=h27cfd23_0
+ - libtiff=4.2.0=h2818925_1
+ - libtool=2.4.6=h295c915_1008
+ - libunistring=0.9.10=h27cfd23_0
+ - libuuid=1.0.3=h7f8727e_2
+ - libuv=1.40.0=h7b6447c_0
+ - libvpx=1.7.0=h439df22_0
+ - libwebp=1.2.2=h55f646e_0
+ - libwebp-base=1.2.2=h7f8727e_0
+ - libxcb=1.15=h7f8727e_0
+ - libxgboost=1.5.1=cpu_h3d145d1_2
+ - libxml2=2.9.14=h74e7548_0
+ - lime=0.2.0.1=pyh9f0ad1d_0
+ - lz4-c=1.9.3=h295c915_1
+ - lzo=2.10=h7b6447c_2
+ - markdown=3.3.4=py39h06a4308_0
+ - markupsafe=2.1.1=py39h7f8727e_0
+ - matplotlib=3.5.1=py39h06a4308_1
+ - matplotlib-base=3.5.1=py39ha18d171_1
+ - matplotlib-inline=0.1.2=pyhd3eb1b0_2
+ - mistune=0.8.4=py39h27cfd23_1000
+ - mkl=2021.4.0=h06a4308_640
+ - mkl-service=2.4.0=py39h7f8727e_0
+ - mkl_fft=1.3.1=py39hd3c417c_0
+ - mkl_random=1.2.2=py39h51133e4_0
+ - mock=4.0.3=pyhd3eb1b0_0
+ - multidict=5.2.0=py39h7f8727e_2
+ - munkres=1.1.4=py_0
+ - nbclient=0.5.13=py39h06a4308_0
+ - nbconvert=6.4.4=py39h06a4308_0
+ - nbformat=5.3.0=py39h06a4308_0
+ - ncurses=6.3=h7f8727e_2
+ - nest-asyncio=1.5.5=py39h06a4308_0
+ - nettle=3.7.3=hbbd107a_1
+ - ninja=1.10.2=h06a4308_5
+ - ninja-base=1.10.2=hd09550d_5
+ - notebook=6.4.11=py39h06a4308_0
+ - numexpr=2.8.1=py39h6abb31d_0
+ - numpy-base=1.21.5=py39ha15fc14_3
+ - oauthlib=3.2.0=pyhd3eb1b0_0
+ - openh264=2.1.1=h4ff587b_0
+ - openpyxl=3.0.10=py39h5eee18b_0
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+ - opt_einsum=3.3.0=pyhd3eb1b0_1
+ - packaging=21.3=pyhd3eb1b0_0
+ - pandas=1.4.2=py39h295c915_0
+ - pandocfilters=1.5.0=pyhd3eb1b0_0
+ - pango=1.50.7=h05da053_0
+ - parso=0.8.3=pyhd3eb1b0_0
+ - pathtools=0.1.2=pyhd3eb1b0_1
+ - pcre=8.45=h295c915_0
+ - pexpect=4.8.0=pyhd3eb1b0_3
+ - pickleshare=0.7.5=pyhd3eb1b0_1003
+ - pillow=9.0.1=py39h22f2fdc_0
+ - pip=21.2.4=py39h06a4308_0
+ - pixman=0.40.0=h7f8727e_1
+ - portalocker=2.3.0=py39h06a4308_0
+ - prometheus_client=0.13.1=pyhd3eb1b0_0
+ - promise=2.3=py39h06a4308_0
+ - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+ - prompt_toolkit=3.0.20=hd3eb1b0_0
+ - psutil=5.8.0=py39h27cfd23_1
+ - ptyprocess=0.7.0=pyhd3eb1b0_2
+ - pure_eval=0.2.2=pyhd3eb1b0_0
+ - py-xgboost=1.5.1=cpu_py39h4655687_2
+ - pyasn1=0.4.8=pyhd3eb1b0_0
+ - pyasn1-modules=0.2.8=py_0
+ - pycocotools=2.0.2=py39hce5d2b2_2
+ - pycparser=2.21=pyhd3eb1b0_0
+ - pydot=1.4.2=py39hf3d152e_2
+ - pygments=2.11.2=pyhd3eb1b0_0
+ - pyjwt=2.1.0=py39h06a4308_0
+ - pyopenssl=21.0.0=pyhd3eb1b0_1
+ - pyqt=5.9.2=py39h2531618_6
+ - pyrsistent=0.18.0=py39heee7806_0
+ - pysocks=1.7.1=py39h06a4308_0
+ - pytables=3.6.1=py39h77479fe_1
+ - pythia8=8.305=py39he80948d_0
+ - python=3.9.12=h12debd9_1
+ - python-dateutil=2.8.2=pyhd3eb1b0_0
+ - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
+ - python-graphviz=0.20.1=pyh22cad53_0
+ - python_abi=3.9=2_cp39
+ - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0
+ - pytorch-model-summary=0.1.1=py_0
+ - pytorch-mutex=1.0=cuda
+ - pytz=2022.1=py39h06a4308_0
+ - pyyaml=6.0=py39h7f8727e_1
+ - pyzmq=22.3.0=py39h295c915_2
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+ - readline=8.1.2=h7f8727e_1
+ - requests=2.27.1=pyhd3eb1b0_0
+ - requests-oauthlib=1.3.0=py_0
+ - rsa=4.7.2=pyhd3eb1b0_1
+ - scikit-learn=1.0.2=py39h51133e4_1
+ - scipy=1.7.3=py39hc147768_0
+ - seaborn=0.11.2=pyhd3eb1b0_0
+ - send2trash=1.8.0=pyhd3eb1b0_1
+ - sentry-sdk=1.5.12=pyhd8ed1ab_0
+ - setproctitle=1.2.2=py39h27cfd23_1004
+ - shap=0.40.0=py39hde0f152_1
+ - shortuuid=1.0.8=py39hf3d152e_0
+ - sip=4.19.13=py39h295c915_0
+ - slicer=0.0.7=pyhd3eb1b0_0
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+ - smmap=4.0.0=pyhd3eb1b0_0
+ - soupsieve=2.3.1=pyhd3eb1b0_0
+ - sqlite=3.38.3=hc218d9a_0
+ - stack_data=0.2.0=pyhd3eb1b0_0
+ - tabulate=0.8.9=py39h06a4308_0
+ - tbb=2021.5.0=hd09550d_0
+ - tensorboard-plugin-wit=1.6.0=py_0
+ - termcolor=1.1.0=py39h06a4308_1
+ - terminado=0.13.1=py39h06a4308_0
+ - testpath=0.5.0=pyhd3eb1b0_0
+ - threadpoolctl=2.2.0=pyh0d69192_0
+ - tk=8.6.12=h1ccaba5_0
+ - torchaudio=0.11.0=py39_cu113
+ - torchinfo=1.6.5=pyhd8ed1ab_0
+ - torchvision=0.12.0=py39_cu113
+ - tornado=6.1=py39h27cfd23_0
+ - tqdm=4.64.0=py39h06a4308_0
+ - traitlets=5.1.1=pyhd3eb1b0_0
+ - typing_extensions=4.1.1=pyh06a4308_0
+ - tzdata=2022a=hda174b7_0
+ - uproot-methods=0.9.2=pyhd8ed1ab_0
+ - urllib3=1.26.9=py39h06a4308_0
+ - wandb=0.12.15=pyhd8ed1ab_0
+ - wcwidth=0.2.5=pyhd3eb1b0_0
+ - webencodings=0.5.1=py39h06a4308_1
+ - werkzeug=2.0.3=pyhd3eb1b0_0
+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
+ - xz=5.2.5=h7f8727e_1
+ - yacs=0.1.6=pyhd3eb1b0_1
+ - yaml=0.2.5=h7b6447c_0
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+ - zeromq=4.3.4=h2531618_0
+ - zipp=3.8.0=py39h06a4308_0
+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/config.yaml b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b691bd695e940fd3ef2ce290a318d2d980ce5246
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677453663
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: -1
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp6
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 30
diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/output.log b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8e32f0665ab911a5e09eaf85780e153f5183a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/requirements.txt b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d242320cdd3539d5d0f942265bb0dfbb8e456252
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:21:12.067241",
+ "startedAt": "2023-02-26T23:21:03.634748",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "-1",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "30"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..8bf99d152ad35c3699ec8600ecb8b169d4e35875
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172103-skuwodjc/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb": {"runtime": 11}}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_172103-skuwodjc/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..ed6a62170bab488c20e84c4810202ffa889c16a7
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172103-skuwodjc/logs/debug-internal.log
@@ -0,0 +1,147 @@
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diff --git a/yolov5_model/wandb/run-20230226_172103-skuwodjc/logs/debug.log b/yolov5_model/wandb/run-20230226_172103-skuwodjc/logs/debug.log
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+++ b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/files/config.yaml
@@ -0,0 +1,170 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677453744
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: -1
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp7
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5s.pt
+workers:
+ desc: null
+ value: 20
diff --git a/yolov5_model/wandb/run-20230226_172224-bz7c0e77/files/output.log b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..f7cee09bb66c6dc7a987ba924ab87a5c06d12fd1
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/files/output.log
@@ -0,0 +1,54 @@
+Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...
+100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.1M/14.1M [00:00<00:00, 38.3MB/s]
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
+ 1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
+ 2 -1 1 18816 models.common.C3 [64, 64, 1]
+ 3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
+ 4 -1 2 115712 models.common.C3 [128, 128, 2]
+ 5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
+ 6 -1 3 625152 models.common.C3 [256, 256, 3]
+ 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
+ 8 -1 1 1182720 models.common.C3 [512, 512, 1]
+ 9 -1 1 656896 models.common.SPPF [512, 512, 5]
+ 10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 1 361984 models.common.C3 [512, 256, 1, False]
+ 14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 1 90880 models.common.C3 [256, 128, 1, False]
+ 18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 1 296448 models.common.C3 [256, 256, 1, False]
+ 21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
+ 24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
+Model summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172224-bz7c0e77/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/files/wandb-summary.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172224-bz7c0e77/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..7f574b797493cdc8089e548199c5fe2cc3d173fe
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+++ b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/logs/debug-internal.log
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new file mode 100644
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+
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diff --git a/yolov5_model/wandb/run-20230226_172224-bz7c0e77/run-bz7c0e77.wandb b/yolov5_model/wandb/run-20230226_172224-bz7c0e77/run-bz7c0e77.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/conda-environment.yaml
@@ -0,0 +1,332 @@
+name: akhot2
+channels:
+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
+dependencies:
+ - _libgcc_mutex=0.1=main
+ - _openmp_mutex=5.1=1_gnu
+ - _py-xgboost-mutex=2.0=cpu_0
+ - _tflow_select=2.3.0=mkl
+ - aiohttp=3.8.1=py39h7f8727e_1
+ - aiosignal=1.2.0=pyhd3eb1b0_0
+ - argon2-cffi=21.3.0=pyhd3eb1b0_0
+ - argon2-cffi-bindings=21.2.0=py39h7f8727e_0
+ - astor=0.8.1=py39h06a4308_0
+ - asttokens=2.0.5=pyhd3eb1b0_0
+ - astunparse=1.6.3=py_0
+ - async-timeout=4.0.1=pyhd3eb1b0_0
+ - atk-1.0=2.36.0=ha1a6a79_0
+ - attrs=21.4.0=pyhd3eb1b0_0
+ - awkward=0.15.0=pyhd3deb0d_0
+ - backcall=0.2.0=pyhd3eb1b0_0
+ - beautifulsoup4=4.11.1=py39h06a4308_0
+ - blas=1.0=mkl
+ - bleach=4.1.0=pyhd3eb1b0_0
+ - blinker=1.4=py39h06a4308_0
+ - blosc=1.21.0=h4ff587b_1
+ - bottleneck=1.3.4=py39hce1f21e_0
+ - brotli=1.0.9=he6710b0_2
+ - brotlipy=0.7.0=py39h27cfd23_1003
+ - bzip2=1.0.8=h7b6447c_0
+ - c-ares=1.18.1=h7f8727e_0
+ - ca-certificates=2023.01.10=h06a4308_0
+ - cachetools=4.2.2=pyhd3eb1b0_0
+ - cairo=1.16.0=h19f5f5c_2
+ - certifi=2022.12.7=pyhd8ed1ab_0
+ - cffi=1.15.0=py39hd667e15_1
+ - charset-normalizer=2.0.4=pyhd3eb1b0_0
+ - click=8.0.4=py39h06a4308_0
+ - cloudpickle=2.0.0=pyhd3eb1b0_0
+ - colorama=0.4.4=pyhd3eb1b0_0
+ - cramjam=2.5.0=py39h860a657_0
+ - cryptography=3.4.8=py39hd23ed53_0
+ - cudatoolkit=11.3.1=h2bc3f7f_2
+ - cython=0.29.28=py39h295c915_0
+ - dataclasses=0.8=pyh6d0b6a4_7
+ - dbus=1.13.18=hb2f20db_0
+ - debugpy=1.5.1=py39h295c915_0
+ - decorator=5.1.1=pyhd3eb1b0_0
+ - defusedxml=0.7.1=pyhd3eb1b0_0
+ - detectron2=0.5=py39hf2442f4_1
+ - docker-pycreds=0.4.0=pyhd3eb1b0_0
+ - entrypoints=0.4=py39h06a4308_0
+ - et_xmlfile=1.1.0=py39h06a4308_0
+ - executing=0.8.3=pyhd3eb1b0_0
+ - expat=2.4.4=h295c915_0
+ - fastparquet=0.8.1=py39hd257fcd_0
+ - ffmpeg=4.2.2=h20bf706_0
+ - filelock=3.7.1=pyhd8ed1ab_0
+ - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
+ - font-ttf-inconsolata=2.001=hcb22688_0
+ - font-ttf-source-code-pro=2.030=hd3eb1b0_0
+ - font-ttf-ubuntu=0.83=h8b1ccd4_0
+ - fontconfig=2.13.1=h6c09931_0
+ - fonts-anaconda=1=h8fa9717_0
+ - fonts-conda-ecosystem=1=hd3eb1b0_0
+ - fonttools=4.25.0=pyhd3eb1b0_0
+ - freetype=2.11.0=h70c0345_0
+ - fribidi=1.0.10=h7b6447c_0
+ - frozenlist=1.2.0=py39h7f8727e_0
+ - fsspec=2022.5.0=pyhd8ed1ab_0
+ - future=0.18.2=py39h06a4308_1
+ - fvcore=0.1.5.post20220506=pyhd8ed1ab_0
+ - gdk-pixbuf=2.42.8=h433bba3_0
+ - gdown=4.5.1=pyhd8ed1ab_0
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+ - giflib=5.2.1=h7b6447c_0
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+ - glib=2.69.1=h4ff587b_1
+ - gmp=6.2.1=h295c915_3
+ - gnutls=3.6.15=he1e5248_0
+ - gobject-introspection=1.68.0=py39he41a700_3
+ - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+ - google-pasta=0.2.0=pyhd3eb1b0_0
+ - graphite2=1.3.14=h295c915_1
+ - graphviz=2.50.0=h3cd0ef9_0
+ - gst-plugins-base=1.14.0=h8213a91_2
+ - gstreamer=1.14.0=h28cd5cc_2
+ - gtk2=2.24.33=h73c1081_2
+ - gts=0.7.6=hb67d8dd_3
+ - h5py=3.6.0=py39ha0f2276_0
+ - harfbuzz=4.3.0=hd55b92a_0
+ - hdf5=1.10.6=hb1b8bf9_0
+ - icu=58.2=he6710b0_3
+ - importlib-metadata=4.11.3=py39h06a4308_0
+ - intel-openmp=2021.4.0=h06a4308_3561
+ - ipykernel=6.9.1=py39h06a4308_0
+ - ipython=8.3.0=py39h06a4308_0
+ - ipython_genutils=0.2.0=pyhd3eb1b0_1
+ - ipywidgets=7.6.5=pyhd3eb1b0_1
+ - jedi=0.18.1=py39h06a4308_1
+ - jinja2=3.0.3=pyhd3eb1b0_0
+ - joblib=1.1.0=pyhd3eb1b0_0
+ - jpeg=9e=h7f8727e_0
+ - jsonschema=4.4.0=py39h06a4308_0
+ - jupyter=1.0.0=py39h06a4308_7
+ - jupyter_client=7.2.2=py39h06a4308_0
+ - jupyter_console=6.4.3=pyhd3eb1b0_0
+ - jupyter_core=4.10.0=py39h06a4308_0
+ - jupyterlab_pygments=0.1.2=py_0
+ - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+ - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+ - kiwisolver=1.4.2=py39h295c915_0
+ - lame=3.100=h7b6447c_0
+ - lcms2=2.12=h3be6417_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgd=2.3.3=h695aa2c_1
+ - libgfortran-ng=7.5.0=ha8ba4b0_17
+ - libgfortran4=7.5.0=ha8ba4b0_17
+ - libgomp=11.2.0=h1234567_1
+ - libidn2=2.3.2=h7f8727e_0
+ - libllvm11=11.1.0=h3826bc1_1
+ - libopus=1.3.1=h7b6447c_0
+ - libpng=1.6.37=hbc83047_0
+ - libprotobuf=3.20.3=he621ea3_0
+ - librsvg=2.54.4=h19fe530_0
+ - libsodium=1.0.18=h7b6447c_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtasn1=4.16.0=h27cfd23_0
+ - libtiff=4.2.0=h2818925_1
+ - libtool=2.4.6=h295c915_1008
+ - libunistring=0.9.10=h27cfd23_0
+ - libuuid=1.0.3=h7f8727e_2
+ - libuv=1.40.0=h7b6447c_0
+ - libvpx=1.7.0=h439df22_0
+ - libwebp=1.2.2=h55f646e_0
+ - libwebp-base=1.2.2=h7f8727e_0
+ - libxcb=1.15=h7f8727e_0
+ - libxgboost=1.5.1=cpu_h3d145d1_2
+ - libxml2=2.9.14=h74e7548_0
+ - lime=0.2.0.1=pyh9f0ad1d_0
+ - lz4-c=1.9.3=h295c915_1
+ - lzo=2.10=h7b6447c_2
+ - markdown=3.3.4=py39h06a4308_0
+ - markupsafe=2.1.1=py39h7f8727e_0
+ - matplotlib=3.5.1=py39h06a4308_1
+ - matplotlib-base=3.5.1=py39ha18d171_1
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+ - mkl-service=2.4.0=py39h7f8727e_0
+ - mkl_fft=1.3.1=py39hd3c417c_0
+ - mkl_random=1.2.2=py39h51133e4_0
+ - mock=4.0.3=pyhd3eb1b0_0
+ - multidict=5.2.0=py39h7f8727e_2
+ - munkres=1.1.4=py_0
+ - nbclient=0.5.13=py39h06a4308_0
+ - nbconvert=6.4.4=py39h06a4308_0
+ - nbformat=5.3.0=py39h06a4308_0
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+ - nest-asyncio=1.5.5=py39h06a4308_0
+ - nettle=3.7.3=hbbd107a_1
+ - ninja=1.10.2=h06a4308_5
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+ - notebook=6.4.11=py39h06a4308_0
+ - numexpr=2.8.1=py39h6abb31d_0
+ - numpy-base=1.21.5=py39ha15fc14_3
+ - oauthlib=3.2.0=pyhd3eb1b0_0
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+ - packaging=21.3=pyhd3eb1b0_0
+ - pandas=1.4.2=py39h295c915_0
+ - pandocfilters=1.5.0=pyhd3eb1b0_0
+ - pango=1.50.7=h05da053_0
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+ - pathtools=0.1.2=pyhd3eb1b0_1
+ - pcre=8.45=h295c915_0
+ - pexpect=4.8.0=pyhd3eb1b0_3
+ - pickleshare=0.7.5=pyhd3eb1b0_1003
+ - pillow=9.0.1=py39h22f2fdc_0
+ - pip=21.2.4=py39h06a4308_0
+ - pixman=0.40.0=h7f8727e_1
+ - portalocker=2.3.0=py39h06a4308_0
+ - prometheus_client=0.13.1=pyhd3eb1b0_0
+ - promise=2.3=py39h06a4308_0
+ - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+ - prompt_toolkit=3.0.20=hd3eb1b0_0
+ - psutil=5.8.0=py39h27cfd23_1
+ - ptyprocess=0.7.0=pyhd3eb1b0_2
+ - pure_eval=0.2.2=pyhd3eb1b0_0
+ - py-xgboost=1.5.1=cpu_py39h4655687_2
+ - pyasn1=0.4.8=pyhd3eb1b0_0
+ - pyasn1-modules=0.2.8=py_0
+ - pycocotools=2.0.2=py39hce5d2b2_2
+ - pycparser=2.21=pyhd3eb1b0_0
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+ - pygments=2.11.2=pyhd3eb1b0_0
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+ - pysocks=1.7.1=py39h06a4308_0
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+ - python-dateutil=2.8.2=pyhd3eb1b0_0
+ - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
+ - python-graphviz=0.20.1=pyh22cad53_0
+ - python_abi=3.9=2_cp39
+ - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0
+ - pytorch-model-summary=0.1.1=py_0
+ - pytorch-mutex=1.0=cuda
+ - pytz=2022.1=py39h06a4308_0
+ - pyyaml=6.0=py39h7f8727e_1
+ - pyzmq=22.3.0=py39h295c915_2
+ - qt=5.9.7=h5867ecd_1
+ - qtconsole=5.3.0=pyhd3eb1b0_0
+ - qtpy=2.0.1=pyhd3eb1b0_0
+ - rclone=1.61.1=h519d9b9_0
+ - readline=8.1.2=h7f8727e_1
+ - requests=2.27.1=pyhd3eb1b0_0
+ - requests-oauthlib=1.3.0=py_0
+ - rsa=4.7.2=pyhd3eb1b0_1
+ - scikit-learn=1.0.2=py39h51133e4_1
+ - scipy=1.7.3=py39hc147768_0
+ - seaborn=0.11.2=pyhd3eb1b0_0
+ - send2trash=1.8.0=pyhd3eb1b0_1
+ - sentry-sdk=1.5.12=pyhd8ed1ab_0
+ - setproctitle=1.2.2=py39h27cfd23_1004
+ - shap=0.40.0=py39hde0f152_1
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+ - sip=4.19.13=py39h295c915_0
+ - slicer=0.0.7=pyhd3eb1b0_0
+ - smart_open=5.2.1=py39h06a4308_0
+ - smmap=4.0.0=pyhd3eb1b0_0
+ - soupsieve=2.3.1=pyhd3eb1b0_0
+ - sqlite=3.38.3=hc218d9a_0
+ - stack_data=0.2.0=pyhd3eb1b0_0
+ - tabulate=0.8.9=py39h06a4308_0
+ - tbb=2021.5.0=hd09550d_0
+ - tensorboard-plugin-wit=1.6.0=py_0
+ - termcolor=1.1.0=py39h06a4308_1
+ - terminado=0.13.1=py39h06a4308_0
+ - testpath=0.5.0=pyhd3eb1b0_0
+ - threadpoolctl=2.2.0=pyh0d69192_0
+ - tk=8.6.12=h1ccaba5_0
+ - torchaudio=0.11.0=py39_cu113
+ - torchinfo=1.6.5=pyhd8ed1ab_0
+ - torchvision=0.12.0=py39_cu113
+ - tornado=6.1=py39h27cfd23_0
+ - tqdm=4.64.0=py39h06a4308_0
+ - traitlets=5.1.1=pyhd3eb1b0_0
+ - typing_extensions=4.1.1=pyh06a4308_0
+ - tzdata=2022a=hda174b7_0
+ - uproot-methods=0.9.2=pyhd8ed1ab_0
+ - urllib3=1.26.9=py39h06a4308_0
+ - wandb=0.12.15=pyhd8ed1ab_0
+ - wcwidth=0.2.5=pyhd3eb1b0_0
+ - webencodings=0.5.1=py39h06a4308_1
+ - werkzeug=2.0.3=pyhd3eb1b0_0
+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
+ - xz=5.2.5=h7f8727e_1
+ - yacs=0.1.6=pyhd3eb1b0_1
+ - yaml=0.2.5=h7b6447c_0
+ - yarl=1.6.3=py39h27cfd23_0
+ - zeromq=4.3.4=h2531618_0
+ - zipp=3.8.0=py39h06a4308_0
+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/config.yaml b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2490e108a7c2b66edcfbe0db1414a1bf72e1402b
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677453950
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 16
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp8
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 40
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diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/output.log b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..ec33145660035e69646bceb3a02d233af433611f
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/output.log
@@ -0,0 +1,265 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Transferred 475/481 items from yolov5m.pt
+[34m[1mAMP: [39m[22mchecks passed ✅
+[34m[1moptimizer:[39m[22m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
+WARNING âš ï¸ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
+See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
+[34m[1mtrain: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels... 1001 images, 181 backgrounds, 0 corrupt: 100%|██████████| 1001/1001 [00:02<00:00, 442.85it/s]
+[34m[1mtrain: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels.cache
+[34m[1mval: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels... 249 images, 42 backgrounds, 0 corrupt: 100%|██████████| 249/249 [00:00<00:00, 421.68it/s]
+[34m[1mval: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels.cache
+[34m[1mAutoAnchor: [39m[22m5.97 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
+Plotting labels to runs/train/exp8/labels.jpg...
+Image sizes 1280 train, 1280 val
+Using 16 dataloader workers
+Logging results to [1mruns/train/exp8
+Starting training for 30 epochs...
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 0/29 13G 0.1003 0.05462 0 39 1280: 100%|██████████| 63/63 [01:25<00:00, 1.35s/it]
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.32it/s]
+ all 249 546 0.107 0.516 0.0915 0.0228
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1/29 16G 0.08137 0.03022 0 27 1280: 100%|██████████| 63/63 [01:21<00:00, 1.29s/it]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.56it/s]
+ all 249 546 0.0788 0.427 0.0727 0.0202
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 2/29 16G 0.07807 0.02594 0 39 1280: 100%|██████████| 63/63 [01:06<00:00, 1.05s/it]
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.72it/s]
+ all 249 546 0.178 0.619 0.172 0.0593
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 3/29 16G 0.06989 0.02582 0 57 1280: 100%|██████████| 63/63 [01:16<00:00, 1.21s/it]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.78it/s]
+ all 249 546 0.162 0.748 0.161 0.0538
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 4/29 16G 0.06044 0.02575 0 47 1280: 100%|██████████| 63/63 [01:07<00:00, 1.07s/it]
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.67it/s]
+ all 249 546 0.483 0.595 0.508 0.134
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 5/29 16G 0.05542 0.02455 0 31 1280: 100%|██████████| 63/63 [00:57<00:00, 1.09it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.25it/s]
+ all 249 546 0.343 0.797 0.345 0.099
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 6/29 16G 0.05063 0.02355 0 43 1280: 100%|██████████| 63/63 [00:56<00:00, 1.11it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.34it/s]
+ all 249 546 0.514 0.974 0.672 0.303
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 7/29 16G 0.04899 0.02446 0 63 1280: 62%|██████■| 39/63 [02:07<01:18, 3.28s/it]
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 322, in train
+ scaler.unscale_(optimizer) # unscale gradients
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py", line 279, in unscale_
+ optimizer_state["found_inf_per_device"] = self._unscale_grads_(optimizer, inv_scale, found_inf, False)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py", line 225, in _unscale_grads_
+ per_device_found_inf.get(device),
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py", line 21, in get
+ retval = self.master.to(device=device, non_blocking=True, copy=True)
+KeyboardInterrupt
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/requirements.txt b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
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+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
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+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
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+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
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+pandocfilters==1.5.0
+parso==0.8.3
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+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
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+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
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+testpath==0.5.0
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+torch==1.11.0
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+tornado==6.1
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+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..af971b80a49543a4918afc7fd0b95191e4723586
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
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+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:26:01.970015",
+ "startedAt": "2023-02-26T23:25:50.126889",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 2,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "16",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "40"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..c3cfeaa7ed8de91ef4a8233c9abfa34a8d87bd2f
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/files/wandb-summary.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..f7777c0b374e4675bfc2349a8f9be9b0cbbaf8fe
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/logs/debug-internal.log
@@ -0,0 +1,556 @@
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diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/logs/debug.log b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/logs/debug.log
new file mode 100644
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diff --git a/yolov5_model/wandb/run-20230226_172550-2fvf94yn/run-2fvf94yn.wandb b/yolov5_model/wandb/run-20230226_172550-2fvf94yn/run-2fvf94yn.wandb
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diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
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+ - ffmpeg=4.2.2=h20bf706_0
+ - filelock=3.7.1=pyhd8ed1ab_0
+ - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
+ - font-ttf-inconsolata=2.001=hcb22688_0
+ - font-ttf-source-code-pro=2.030=hd3eb1b0_0
+ - font-ttf-ubuntu=0.83=h8b1ccd4_0
+ - fontconfig=2.13.1=h6c09931_0
+ - fonts-anaconda=1=h8fa9717_0
+ - fonts-conda-ecosystem=1=hd3eb1b0_0
+ - fonttools=4.25.0=pyhd3eb1b0_0
+ - freetype=2.11.0=h70c0345_0
+ - fribidi=1.0.10=h7b6447c_0
+ - frozenlist=1.2.0=py39h7f8727e_0
+ - fsspec=2022.5.0=pyhd8ed1ab_0
+ - future=0.18.2=py39h06a4308_1
+ - fvcore=0.1.5.post20220506=pyhd8ed1ab_0
+ - gdk-pixbuf=2.42.8=h433bba3_0
+ - gdown=4.5.1=pyhd8ed1ab_0
+ - gensim=4.1.2=py39h295c915_0
+ - giflib=5.2.1=h7b6447c_0
+ - gitdb=4.0.7=pyhd3eb1b0_0
+ - gitpython=3.1.18=pyhd3eb1b0_1
+ - glib=2.69.1=h4ff587b_1
+ - gmp=6.2.1=h295c915_3
+ - gnutls=3.6.15=he1e5248_0
+ - gobject-introspection=1.68.0=py39he41a700_3
+ - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+ - google-pasta=0.2.0=pyhd3eb1b0_0
+ - graphite2=1.3.14=h295c915_1
+ - graphviz=2.50.0=h3cd0ef9_0
+ - gst-plugins-base=1.14.0=h8213a91_2
+ - gstreamer=1.14.0=h28cd5cc_2
+ - gtk2=2.24.33=h73c1081_2
+ - gts=0.7.6=hb67d8dd_3
+ - h5py=3.6.0=py39ha0f2276_0
+ - harfbuzz=4.3.0=hd55b92a_0
+ - hdf5=1.10.6=hb1b8bf9_0
+ - icu=58.2=he6710b0_3
+ - importlib-metadata=4.11.3=py39h06a4308_0
+ - intel-openmp=2021.4.0=h06a4308_3561
+ - ipykernel=6.9.1=py39h06a4308_0
+ - ipython=8.3.0=py39h06a4308_0
+ - ipython_genutils=0.2.0=pyhd3eb1b0_1
+ - ipywidgets=7.6.5=pyhd3eb1b0_1
+ - jedi=0.18.1=py39h06a4308_1
+ - jinja2=3.0.3=pyhd3eb1b0_0
+ - joblib=1.1.0=pyhd3eb1b0_0
+ - jpeg=9e=h7f8727e_0
+ - jsonschema=4.4.0=py39h06a4308_0
+ - jupyter=1.0.0=py39h06a4308_7
+ - jupyter_client=7.2.2=py39h06a4308_0
+ - jupyter_console=6.4.3=pyhd3eb1b0_0
+ - jupyter_core=4.10.0=py39h06a4308_0
+ - jupyterlab_pygments=0.1.2=py_0
+ - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+ - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+ - kiwisolver=1.4.2=py39h295c915_0
+ - lame=3.100=h7b6447c_0
+ - lcms2=2.12=h3be6417_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgd=2.3.3=h695aa2c_1
+ - libgfortran-ng=7.5.0=ha8ba4b0_17
+ - libgfortran4=7.5.0=ha8ba4b0_17
+ - libgomp=11.2.0=h1234567_1
+ - libidn2=2.3.2=h7f8727e_0
+ - libllvm11=11.1.0=h3826bc1_1
+ - libopus=1.3.1=h7b6447c_0
+ - libpng=1.6.37=hbc83047_0
+ - libprotobuf=3.20.3=he621ea3_0
+ - librsvg=2.54.4=h19fe530_0
+ - libsodium=1.0.18=h7b6447c_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtasn1=4.16.0=h27cfd23_0
+ - libtiff=4.2.0=h2818925_1
+ - libtool=2.4.6=h295c915_1008
+ - libunistring=0.9.10=h27cfd23_0
+ - libuuid=1.0.3=h7f8727e_2
+ - libuv=1.40.0=h7b6447c_0
+ - libvpx=1.7.0=h439df22_0
+ - libwebp=1.2.2=h55f646e_0
+ - libwebp-base=1.2.2=h7f8727e_0
+ - libxcb=1.15=h7f8727e_0
+ - libxgboost=1.5.1=cpu_h3d145d1_2
+ - libxml2=2.9.14=h74e7548_0
+ - lime=0.2.0.1=pyh9f0ad1d_0
+ - lz4-c=1.9.3=h295c915_1
+ - lzo=2.10=h7b6447c_2
+ - markdown=3.3.4=py39h06a4308_0
+ - markupsafe=2.1.1=py39h7f8727e_0
+ - matplotlib=3.5.1=py39h06a4308_1
+ - matplotlib-base=3.5.1=py39ha18d171_1
+ - matplotlib-inline=0.1.2=pyhd3eb1b0_2
+ - mistune=0.8.4=py39h27cfd23_1000
+ - mkl=2021.4.0=h06a4308_640
+ - mkl-service=2.4.0=py39h7f8727e_0
+ - mkl_fft=1.3.1=py39hd3c417c_0
+ - mkl_random=1.2.2=py39h51133e4_0
+ - mock=4.0.3=pyhd3eb1b0_0
+ - multidict=5.2.0=py39h7f8727e_2
+ - munkres=1.1.4=py_0
+ - nbclient=0.5.13=py39h06a4308_0
+ - nbconvert=6.4.4=py39h06a4308_0
+ - nbformat=5.3.0=py39h06a4308_0
+ - ncurses=6.3=h7f8727e_2
+ - nest-asyncio=1.5.5=py39h06a4308_0
+ - nettle=3.7.3=hbbd107a_1
+ - ninja=1.10.2=h06a4308_5
+ - ninja-base=1.10.2=hd09550d_5
+ - notebook=6.4.11=py39h06a4308_0
+ - numexpr=2.8.1=py39h6abb31d_0
+ - numpy-base=1.21.5=py39ha15fc14_3
+ - oauthlib=3.2.0=pyhd3eb1b0_0
+ - openh264=2.1.1=h4ff587b_0
+ - openpyxl=3.0.10=py39h5eee18b_0
+ - openssl=1.1.1s=h7f8727e_0
+ - opt_einsum=3.3.0=pyhd3eb1b0_1
+ - packaging=21.3=pyhd3eb1b0_0
+ - pandas=1.4.2=py39h295c915_0
+ - pandocfilters=1.5.0=pyhd3eb1b0_0
+ - pango=1.50.7=h05da053_0
+ - parso=0.8.3=pyhd3eb1b0_0
+ - pathtools=0.1.2=pyhd3eb1b0_1
+ - pcre=8.45=h295c915_0
+ - pexpect=4.8.0=pyhd3eb1b0_3
+ - pickleshare=0.7.5=pyhd3eb1b0_1003
+ - pillow=9.0.1=py39h22f2fdc_0
+ - pip=21.2.4=py39h06a4308_0
+ - pixman=0.40.0=h7f8727e_1
+ - portalocker=2.3.0=py39h06a4308_0
+ - prometheus_client=0.13.1=pyhd3eb1b0_0
+ - promise=2.3=py39h06a4308_0
+ - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+ - prompt_toolkit=3.0.20=hd3eb1b0_0
+ - psutil=5.8.0=py39h27cfd23_1
+ - ptyprocess=0.7.0=pyhd3eb1b0_2
+ - pure_eval=0.2.2=pyhd3eb1b0_0
+ - py-xgboost=1.5.1=cpu_py39h4655687_2
+ - pyasn1=0.4.8=pyhd3eb1b0_0
+ - pyasn1-modules=0.2.8=py_0
+ - pycocotools=2.0.2=py39hce5d2b2_2
+ - pycparser=2.21=pyhd3eb1b0_0
+ - pydot=1.4.2=py39hf3d152e_2
+ - pygments=2.11.2=pyhd3eb1b0_0
+ - pyjwt=2.1.0=py39h06a4308_0
+ - pyopenssl=21.0.0=pyhd3eb1b0_1
+ - pyqt=5.9.2=py39h2531618_6
+ - pyrsistent=0.18.0=py39heee7806_0
+ - pysocks=1.7.1=py39h06a4308_0
+ - pytables=3.6.1=py39h77479fe_1
+ - pythia8=8.305=py39he80948d_0
+ - python=3.9.12=h12debd9_1
+ - python-dateutil=2.8.2=pyhd3eb1b0_0
+ - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
+ - python-graphviz=0.20.1=pyh22cad53_0
+ - python_abi=3.9=2_cp39
+ - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0
+ - pytorch-model-summary=0.1.1=py_0
+ - pytorch-mutex=1.0=cuda
+ - pytz=2022.1=py39h06a4308_0
+ - pyyaml=6.0=py39h7f8727e_1
+ - pyzmq=22.3.0=py39h295c915_2
+ - qt=5.9.7=h5867ecd_1
+ - qtconsole=5.3.0=pyhd3eb1b0_0
+ - qtpy=2.0.1=pyhd3eb1b0_0
+ - rclone=1.61.1=h519d9b9_0
+ - readline=8.1.2=h7f8727e_1
+ - requests=2.27.1=pyhd3eb1b0_0
+ - requests-oauthlib=1.3.0=py_0
+ - rsa=4.7.2=pyhd3eb1b0_1
+ - scikit-learn=1.0.2=py39h51133e4_1
+ - scipy=1.7.3=py39hc147768_0
+ - seaborn=0.11.2=pyhd3eb1b0_0
+ - send2trash=1.8.0=pyhd3eb1b0_1
+ - sentry-sdk=1.5.12=pyhd8ed1ab_0
+ - setproctitle=1.2.2=py39h27cfd23_1004
+ - shap=0.40.0=py39hde0f152_1
+ - shortuuid=1.0.8=py39hf3d152e_0
+ - sip=4.19.13=py39h295c915_0
+ - slicer=0.0.7=pyhd3eb1b0_0
+ - smart_open=5.2.1=py39h06a4308_0
+ - smmap=4.0.0=pyhd3eb1b0_0
+ - soupsieve=2.3.1=pyhd3eb1b0_0
+ - sqlite=3.38.3=hc218d9a_0
+ - stack_data=0.2.0=pyhd3eb1b0_0
+ - tabulate=0.8.9=py39h06a4308_0
+ - tbb=2021.5.0=hd09550d_0
+ - tensorboard-plugin-wit=1.6.0=py_0
+ - termcolor=1.1.0=py39h06a4308_1
+ - terminado=0.13.1=py39h06a4308_0
+ - testpath=0.5.0=pyhd3eb1b0_0
+ - threadpoolctl=2.2.0=pyh0d69192_0
+ - tk=8.6.12=h1ccaba5_0
+ - torchaudio=0.11.0=py39_cu113
+ - torchinfo=1.6.5=pyhd8ed1ab_0
+ - torchvision=0.12.0=py39_cu113
+ - tornado=6.1=py39h27cfd23_0
+ - tqdm=4.64.0=py39h06a4308_0
+ - traitlets=5.1.1=pyhd3eb1b0_0
+ - typing_extensions=4.1.1=pyh06a4308_0
+ - tzdata=2022a=hda174b7_0
+ - uproot-methods=0.9.2=pyhd8ed1ab_0
+ - urllib3=1.26.9=py39h06a4308_0
+ - wandb=0.12.15=pyhd8ed1ab_0
+ - wcwidth=0.2.5=pyhd3eb1b0_0
+ - webencodings=0.5.1=py39h06a4308_1
+ - werkzeug=2.0.3=pyhd3eb1b0_0
+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
+ - xz=5.2.5=h7f8727e_1
+ - yacs=0.1.6=pyhd3eb1b0_1
+ - yaml=0.2.5=h7b6447c_0
+ - yarl=1.6.3=py39h27cfd23_0
+ - zeromq=4.3.4=h2531618_0
+ - zipp=3.8.0=py39h06a4308_0
+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/config.yaml b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e4b1604861e2acbd5561700bd6116a543e0fdaed
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/config.yaml
@@ -0,0 +1,170 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677455186
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 16
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp9
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 40
diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/output.log b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8e32f0665ab911a5e09eaf85780e153f5183a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/requirements.txt b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..db099e4f55b386407a370b8a851ae5f49022f0c4
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:47:11.221412",
+ "startedAt": "2023-02-26T23:46:25.832396",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "16",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "40"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/files/wandb-summary.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..8922dd7a127f7a5843e59c8294badcf4ec6206c0
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/logs/debug-internal.log
@@ -0,0 +1,46 @@
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diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/logs/debug.log b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..f796badd6886b0dff5447c5512e195559e46fb8c
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/logs/debug.log
@@ -0,0 +1,32 @@
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+2023-02-26 17:46:26,095 INFO MainThread:247391 [wandb_setup.py:_flush():75] Inferring run settings from compute environment: {'program_relpath': 'yolov5_model/train.py', 'program': '/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py'}
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diff --git a/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/run-2vk3tzl3.wandb b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/run-2vk3tzl3.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..f7e13d31b45c76f2c255058162ce856447ce8e76
Binary files /dev/null and b/yolov5_model/wandb/run-20230226_174625-2vk3tzl3/run-2vk3tzl3.wandb differ
diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f24b6a4a4bd12d22e7aa6cebbe5625966bdb1f20
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/conda-environment.yaml
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+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.6
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/config.yaml b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..09c396a28f3f62b409074f5586e69520a3039789
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677455270
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 1
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp10
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5s.pt
+workers:
+ desc: null
+ value: 40
diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/output.log b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..cbf62f98ee8b4946b8b110ee6712fac336728c13
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
+ 1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
+ 2 -1 1 18816 models.common.C3 [64, 64, 1]
+ 3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
+ 4 -1 2 115712 models.common.C3 [128, 128, 2]
+ 5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
+ 6 -1 3 625152 models.common.C3 [256, 256, 3]
+ 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
+ 8 -1 1 1182720 models.common.C3 [512, 512, 1]
+ 9 -1 1 656896 models.common.SPPF [512, 512, 5]
+ 10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 1 361984 models.common.C3 [512, 256, 1, False]
+ 14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 1 90880 models.common.C3 [256, 128, 1, False]
+ 18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 1 296448 models.common.C3 [256, 256, 1, False]
+ 21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
+ 24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
+Model summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/requirements.txt b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..6485c6e71c1cb168352c3c0bf392973d7fc9dbea
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-26T23:47:57.323741",
+ "startedAt": "2023-02-26T23:47:50.617478",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "1",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5s.pt",
+ "--workers",
+ "40"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1ac4f7d3564b2fd407d247e6957709faa41a169
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb": {"runtime": 9}}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_174750-fys27l4q/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..67858b75c75b116e1d52bea940c0284623f3330f
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/logs/debug-internal.log
@@ -0,0 +1,147 @@
+2023-02-26 17:47:53,504 INFO MainThread:256512 [internal.py:wandb_internal():90] W&B internal server running at pid: 256512, started at: 2023-02-26 17:47:53.503362
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+2023-02-26 17:47:53,509 DEBUG SenderThread:256512 [sender.py:send():232] send: header
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+2023-02-26 17:47:53,687 INFO SenderThread:256512 [sender.py:_start_run_threads():811] run started: fys27l4q with start time 1677455270
+2023-02-26 17:47:53,687 DEBUG SenderThread:256512 [sender.py:send():232] send: summary
+2023-02-26 17:47:53,690 INFO SenderThread:256512 [sender.py:_save_file():946] saving file wandb-summary.json with policy end
+2023-02-26 17:47:53,690 DEBUG SenderThread:256512 [sender.py:send_request():246] send_request: check_version
+2023-02-26 17:47:53,736 DEBUG HandlerThread:256512 [handler.py:handle_request():141] handle_request: run_start
+2023-02-26 17:47:54,693 INFO Thread-7 :256512 [dir_watcher.py:_on_file_created():213] file/dir created: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/wandb/run-20230226_174750-fys27l4q/files/wandb-summary.json
+2023-02-26 17:47:57,323 DEBUG HandlerThread:256512 [meta.py:__init__():35] meta init
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diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/logs/debug.log b/yolov5_model/wandb/run-20230226_174750-fys27l4q/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..4f9e9f8324ae94ff1d95d4002257606b890f672d
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_174750-fys27l4q/logs/debug.log
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diff --git a/yolov5_model/wandb/run-20230226_174750-fys27l4q/run-fys27l4q.wandb b/yolov5_model/wandb/run-20230226_174750-fys27l4q/run-fys27l4q.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..19196846276f5fbc7c3b146f2aeaf0e1426a4b9a
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diff --git a/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/config.yaml b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..06a5405ba5c1e5c9dbf692e083ab27ad72405617
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+++ b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677456816
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
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+ - 16
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+ 8:
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+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
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+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
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+ hsv_h: 0.015
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+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
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+ value: false
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+ value: false
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+ value: false
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+ value: false
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+ value: SGD
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+ value: 100
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+ value: runs/train
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+ value: false
+rect:
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+ value: false
+resume:
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+ value: false
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+ value: runs/train/exp11
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+ value: 0
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+workers:
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+ value: 8
diff --git a/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/output.log b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..7a946b7fdf5fb4ef50262e44823c279036544fa9
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/output.log
@@ -0,0 +1,54 @@
+Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt to yolov5n.pt...
+100%|████████████████████████████████████████████████████████████| 3.87M/3.87M [00:00<00:00, 71.5MB/s]
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 1760 models.common.Conv [3, 16, 6, 2, 2]
+ 1 -1 1 4672 models.common.Conv [16, 32, 3, 2]
+ 2 -1 1 4800 models.common.C3 [32, 32, 1]
+ 3 -1 1 18560 models.common.Conv [32, 64, 3, 2]
+ 4 -1 2 29184 models.common.C3 [64, 64, 2]
+ 5 -1 1 73984 models.common.Conv [64, 128, 3, 2]
+ 6 -1 3 156928 models.common.C3 [128, 128, 3]
+ 7 -1 1 295424 models.common.Conv [128, 256, 3, 2]
+ 8 -1 1 296448 models.common.C3 [256, 256, 1]
+ 9 -1 1 164608 models.common.SPPF [256, 256, 5]
+ 10 -1 1 33024 models.common.Conv [256, 128, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 1 90880 models.common.C3 [256, 128, 1, False]
+ 14 -1 1 8320 models.common.Conv [128, 64, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 1 22912 models.common.C3 [128, 64, 1, False]
+ 18 -1 1 36992 models.common.Conv [64, 64, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 1 74496 models.common.C3 [128, 128, 1, False]
+ 21 -1 1 147712 models.common.Conv [128, 128, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 1 296448 models.common.C3 [256, 256, 1, False]
+ 24 [17, 20, 23] 1 8118 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]]
+Model summary: 214 layers, 1765270 parameters, 1765270 gradients, 4.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/requirements.txt b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/requirements.txt
@@ -0,0 +1,216 @@
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+mistune==0.8.4
+mkl-fft==1.3.1
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\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..19ac2bea8b845505c59828fd01610ee45c97c1eb
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/files/wandb-metadata.json
@@ -0,0 +1,35 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-27T00:13:56.299357",
+ "startedAt": "2023-02-27T00:13:36.648260",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "1",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5n.pt"
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new file mode 100644
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diff --git a/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/logs/debug.log b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/logs/debug.log
new file mode 100644
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+config: {'weights': 'yolov5n.pt', 'cfg': '', 'data': '/projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml', 'hyp': {'lr0': 0.01, 'lrf': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0}, 'epochs': 30, 'batch_size': 1, 'imgsz': 1280, 'rect': False, 'resume': False, 'nosave': False, 'noval': False, 'noautoanchor': False, 'noplots': False, 'evolve': None, 'bucket': '', 'cache': None, 'image_weights': False, 'device': '', 'multi_scale': False, 'single_cls': False, 'optimizer': 'SGD', 'sync_bn': False, 'workers': 8, 'project': 'runs/train', 'name': 'exp', 'exist_ok': False, 'quad': False, 'cos_lr': False, 'label_smoothing': 0.0, 'patience': 100, 'freeze': [0], 'save_period': -1, 'seed': 0, 'local_rank': -1, 'entity': None, 'upload_dataset': False, 'bbox_interval': -1, 'artifact_alias': 'latest', 'save_dir': 'runs/train/exp11'}
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diff --git a/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/run-tzc0g9ld.wandb b/yolov5_model/wandb/run-20230226_181336-tzc0g9ld/run-tzc0g9ld.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..fbe867cae8ae0b6ea2af2cc325fa7e38bcf005e5
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diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/conda-environment.yaml
@@ -0,0 +1,332 @@
+name: akhot2
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+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
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+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/config.yaml b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bd22410cd8d3f4f7b5aecda27b6b754e67fae062
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/config.yaml
@@ -0,0 +1,176 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677456928
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 1
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp12
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5n.pt
+workers:
+ desc: null
+ value: 8
diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/output.log b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..0d57afde8b955f5cba169b0d25dd009bfdee0887
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 1760 models.common.Conv [3, 16, 6, 2, 2]
+ 1 -1 1 4672 models.common.Conv [16, 32, 3, 2]
+ 2 -1 1 4800 models.common.C3 [32, 32, 1]
+ 3 -1 1 18560 models.common.Conv [32, 64, 3, 2]
+ 4 -1 2 29184 models.common.C3 [64, 64, 2]
+ 5 -1 1 73984 models.common.Conv [64, 128, 3, 2]
+ 6 -1 3 156928 models.common.C3 [128, 128, 3]
+ 7 -1 1 295424 models.common.Conv [128, 256, 3, 2]
+ 8 -1 1 296448 models.common.C3 [256, 256, 1]
+ 9 -1 1 164608 models.common.SPPF [256, 256, 5]
+ 10 -1 1 33024 models.common.Conv [256, 128, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 1 90880 models.common.C3 [256, 128, 1, False]
+ 14 -1 1 8320 models.common.Conv [128, 64, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 1 22912 models.common.C3 [128, 64, 1, False]
+ 18 -1 1 36992 models.common.Conv [64, 64, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 1 74496 models.common.C3 [128, 128, 1, False]
+ 21 -1 1 147712 models.common.Conv [128, 128, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 1 296448 models.common.C3 [256, 256, 1, False]
+ 24 [17, 20, 23] 1 8118 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]]
+Model summary: 214 layers, 1765270 parameters, 1765270 gradients, 4.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/requirements.txt b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..ff2c66c13aac222122522c45460d1f7d0ab311eb
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/wandb-metadata.json
@@ -0,0 +1,35 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-27T00:15:32.896258",
+ "startedAt": "2023-02-27T00:15:28.428961",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "1",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5n.pt"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..a594e25ada167c5ad54a828e8a5b1a6223620b44
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb": {"runtime": 8}}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..b833e11c730f067bea8a312e370e57ba63cb489a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/logs/debug-internal.log
@@ -0,0 +1,148 @@
+2023-02-26 18:15:30,161 INFO MainThread:103361 [internal.py:wandb_internal():90] W&B internal server running at pid: 103361, started at: 2023-02-26 18:15:30.160173
+2023-02-26 18:15:30,165 INFO WriterThread:103361 [datastore.py:open_for_write():75] open: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/wandb/run-20230226_181528-3aai7fdf/run-3aai7fdf.wandb
+2023-02-26 18:15:30,167 DEBUG SenderThread:103361 [sender.py:send():232] send: header
+2023-02-26 18:15:30,168 DEBUG SenderThread:103361 [sender.py:send():232] send: run
+2023-02-26 18:15:30,177 INFO SenderThread:103361 [sender.py:_maybe_setup_resume():489] checking resume status for None/YOLOv5/3aai7fdf
+2023-02-26 18:15:30,372 DEBUG HandlerThread:103361 [handler.py:handle_request():141] handle_request: check_version
+2023-02-26 18:15:30,380 INFO SenderThread:103361 [dir_watcher.py:__init__():166] watching files in: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files
+2023-02-26 18:15:30,380 INFO SenderThread:103361 [sender.py:_start_run_threads():811] run started: 3aai7fdf with start time 1677456928
+2023-02-26 18:15:30,380 DEBUG SenderThread:103361 [sender.py:send():232] send: summary
+2023-02-26 18:15:30,381 INFO SenderThread:103361 [sender.py:_save_file():946] saving file wandb-summary.json with policy end
+2023-02-26 18:15:30,382 DEBUG SenderThread:103361 [sender.py:send_request():246] send_request: check_version
+2023-02-26 18:15:30,428 DEBUG HandlerThread:103361 [handler.py:handle_request():141] handle_request: run_start
+2023-02-26 18:15:31,432 INFO Thread-7 :103361 [dir_watcher.py:_on_file_created():213] file/dir created: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/wandb/run-20230226_181528-3aai7fdf/files/wandb-summary.json
+2023-02-26 18:15:32,895 DEBUG HandlerThread:103361 [meta.py:__init__():35] meta init
+2023-02-26 18:15:32,896 DEBUG HandlerThread:103361 [meta.py:__init__():49] meta init done
+2023-02-26 18:15:32,896 DEBUG HandlerThread:103361 [meta.py:probe():209] probe
+2023-02-26 18:15:32,904 DEBUG HandlerThread:103361 [meta.py:_setup_git():199] setup git
+2023-02-26 18:15:32,952 DEBUG HandlerThread:103361 [meta.py:_setup_git():206] setup git done
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diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/logs/debug.log b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..e105419881228a5bf6639bcd6da2980b3082b05d
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/logs/debug.log
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diff --git a/yolov5_model/wandb/run-20230226_181528-3aai7fdf/run-3aai7fdf.wandb b/yolov5_model/wandb/run-20230226_181528-3aai7fdf/run-3aai7fdf.wandb
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index 0000000000000000000000000000000000000000..fee51f2e453b7a2132f01d00787d06ac3d182069
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diff --git a/yolov5_model/wandb/run-20230226_192141-shtac223/files/conda-environment.yaml b/yolov5_model/wandb/run-20230226_192141-shtac223/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192141-shtac223/files/conda-environment.yaml
@@ -0,0 +1,332 @@
+name: akhot2
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+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
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+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230226_192141-shtac223/files/config.yaml b/yolov5_model/wandb/run-20230226_192141-shtac223/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2ea5e9f32e11ccde4262502241b58be136a617a8
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192141-shtac223/files/config.yaml
@@ -0,0 +1,170 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1677460901
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 16
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 30
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
+ desc: null
+ value: 0.0
+local_rank:
+ desc: null
+ value: -1
+multi_scale:
+ desc: null
+ value: false
+name:
+ desc: null
+ value: exp
+noautoanchor:
+ desc: null
+ value: false
+noplots:
+ desc: null
+ value: false
+nosave:
+ desc: null
+ value: false
+noval:
+ desc: null
+ value: false
+optimizer:
+ desc: null
+ value: SGD
+patience:
+ desc: null
+ value: 100
+project:
+ desc: null
+ value: runs/train
+quad:
+ desc: null
+ value: false
+rect:
+ desc: null
+ value: false
+resume:
+ desc: null
+ value: false
+save_dir:
+ desc: null
+ value: runs/train/exp13
+save_period:
+ desc: null
+ value: -1
+seed:
+ desc: null
+ value: 0
+single_cls:
+ desc: null
+ value: false
+sync_bn:
+ desc: null
+ value: false
+upload_dataset:
+ desc: null
+ value: false
+weights:
+ desc: null
+ value: yolov5m.pt
+workers:
+ desc: null
+ value: 40
diff --git a/yolov5_model/wandb/run-20230226_192141-shtac223/files/output.log b/yolov5_model/wandb/run-20230226_192141-shtac223/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8e32f0665ab911a5e09eaf85780e153f5183a
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192141-shtac223/files/output.log
@@ -0,0 +1,52 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Traceback (most recent call last):
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 640, in <module>
+ main(opt)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 529, in main
+ train(opt.hyp, opt, device, callbacks)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py", line 125, in train
+ model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 907, in to
+ return self._apply(convert)
+ File "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/models/yolo.py", line 155, in _apply
+ self = super()._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 578, in _apply
+ module._apply(fn)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 601, in _apply
+ param_applied = fn(param)
+ File "/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 905, in convert
+ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_192141-shtac223/files/requirements.txt b/yolov5_model/wandb/run-20230226_192141-shtac223/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192141-shtac223/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
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+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
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+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_192141-shtac223/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_192141-shtac223/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..232aae1a4fbefe72baf8566eb89bd093366663df
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192141-shtac223/files/wandb-metadata.json
@@ -0,0 +1,37 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-02-27T01:22:11.182088",
+ "startedAt": "2023-02-27T01:21:41.103005",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 8,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "16",
+ "--epochs",
+ "30",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "40"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230226_192141-shtac223/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_192141-shtac223/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192141-shtac223/files/wandb-summary.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_192141-shtac223/logs/debug-internal.log b/yolov5_model/wandb/run-20230226_192141-shtac223/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..a560af056a13f8090bde17a86ae0d39d573d0e7e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192141-shtac223/logs/debug-internal.log
@@ -0,0 +1,43 @@
+2023-02-26 19:21:49,468 INFO MainThread:2859 [internal.py:wandb_internal():90] W&B internal server running at pid: 2859, started at: 2023-02-26 19:21:49.423799
+2023-02-26 19:21:49,476 INFO WriterThread:2859 [datastore.py:open_for_write():75] open: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/wandb/run-20230226_192141-shtac223/run-shtac223.wandb
+2023-02-26 19:21:49,481 DEBUG SenderThread:2859 [sender.py:send():232] send: header
+2023-02-26 19:21:49,481 DEBUG SenderThread:2859 [sender.py:send():232] send: run
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+2023-02-26 19:21:49,810 DEBUG HandlerThread:2859 [handler.py:handle_request():141] handle_request: check_version
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+2023-02-26 19:21:49,823 DEBUG SenderThread:2859 [sender.py:send():232] send: summary
+2023-02-26 19:21:49,824 INFO SenderThread:2859 [sender.py:_save_file():946] saving file wandb-summary.json with policy end
+2023-02-26 19:21:49,825 DEBUG SenderThread:2859 [sender.py:send_request():246] send_request: check_version
+2023-02-26 19:21:49,909 DEBUG HandlerThread:2859 [handler.py:handle_request():141] handle_request: run_start
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+ - ptyprocess=0.7.0=pyhd3eb1b0_2
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+ - x264=1!157.20191217=h7b6447c_0
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+ - pip:
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+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
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+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
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+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
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index 0000000000000000000000000000000000000000..d27ad7421c969ea61c8add6e657682f80d2d061e
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+ t:
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+ - 3
+ - 41
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+device:
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+ value: 100
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+ value: runs/train
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+weights:
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+ value: yolov5m.pt
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diff --git a/yolov5_model/wandb/run-20230226_192253-294etqct/files/output.log b/yolov5_model/wandb/run-20230226_192253-294etqct/files/output.log
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+++ b/yolov5_model/wandb/run-20230226_192253-294etqct/files/output.log
@@ -0,0 +1,931 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Transferred 475/481 items from yolov5m.pt
+[34m[1mAMP: [39m[22mchecks passed ✅
+[34m[1moptimizer:[39m[22m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
+WARNING âš ï¸ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
+See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
+[34m[1mtrain: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels... 1001 images, 163 backgrounds, 0 corrupt: 100%|████
+[34m[1mtrain: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels.cache
+[34m[1mval: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels... 249 images, 39 backgrounds, 0 corrupt: 100%|█████████
+[34m[1mval: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels.cache
+[34m[1mAutoAnchor: [39m[22m5.94 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
+Plotting labels to runs/train/exp14/labels.jpg...
+Image sizes 1280 train, 1280 val
+Using 16 dataloader workers
+Logging results to [1mruns/train/exp14
+Starting training for 30 epochs...
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 0/29 13G 0.08853 0.05382 0 35 1280: 100%|██████████| 63/63 [01:01<00:00, 1.02it/s]
+
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:06<00:00, 1.32it/
+ all 249 604 0.364 0.696 0.43 0.128
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 1/29 16G 0.06939 0.0265 0 36 1280: 100%|██████████| 63/63 [02:10<00:00, 2.06s/it]
+
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:05<00:00, 1.43it/
+ all 249 604 0.294 0.942 0.295 0.101
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 2/29 16G 0.06626 0.02028 0 33 1280: 100%|██████████| 63/63 [01:22<00:00, 1.31s/it]
+
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.63it/
+ all 249 604 0.386 0.762 0.4 0.116
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 3/29 16G 0.0584 0.01806 0 54 1280: 100%|██████████| 63/63 [00:59<00:00, 1.05it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.25it/
+ all 249 604 0.448 0.808 0.593 0.268
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 4/29 16G 0.05048 0.01615 0 45 1280: 100%|██████████| 63/63 [01:03<00:00, 1.01s/it]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.40it/
+ all 249 604 0.876 0.97 0.933 0.389
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 5/29 16G 0.04408 0.01462 0 25 1280: 100%|██████████| 63/63 [01:01<00:00, 1.02it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.52it/
+ all 249 604 0.993 0.995 0.994 0.53
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 6/29 16G 0.03883 0.01355 0 47 1280: 100%|██████████| 63/63 [01:01<00:00, 1.02it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.93it/
+ all 249 604 0.999 0.997 0.995 0.683
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 7/29 16G 0.03522 0.01349 0 33 1280: 100%|██████████| 63/63 [01:35<00:00, 1.52s/it]
+
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+
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:29<00:00, 3.63s/i
+ all 249 604 0.985 0.997 0.994 0.558
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
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+ 8/29 16G 0.03303 0.01277 0 57 1280: 100%|██████████| 63/63 [01:41<00:00, 1.60s/it]
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.38it/
+ all 249 604 1 0.996 0.995 0.599
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 9/29 16G 0.03076 0.01208 0 30 1280: 100%|██████████| 63/63 [01:09<00:00, 1.10s/it]
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.23it/
+ all 249 604 1 0.997 0.995 0.682
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+ 10/29 16G 0.02793 0.01174 0 33 1280: 100%|██████████| 63/63 [00:59<00:00, 1.06it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.84it/
+ all 249 604 1 0.997 0.995 0.697
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+ 11/29 16G 0.02742 0.01086 0 69 1280: 14%|█■| 9/63 [00:02<00:12, 4.24it/s]
+ 11/29 16G 0.02813 0.0108 0 44 1280: 21%|██ | 13/63 [00:04<00:17, 2.79it/s]
+ 11/29 16G 0.02796 0.01108 0 60 1280: 29%|██▊ | 18/63 [00:13<00:34, 1.32it/s]
+ 11/29 16G 0.02805 0.01105 0 58 1280: 30%|███ | 19/63 [00:15<00:48, 1.09s/it]
+ 11/29 16G 0.02779 0.01122 0 73 1280: 35%|███■| 22/63 [00:17<00:30, 1.34it/s]
+ 11/29 16G 0.02737 0.01107 0 63 1280: 41%|████■| 26/63 [00:19<00:22, 1.63it/s]
+ 11/29 16G 0.02732 0.01119 0 68 1280: 46%|████▌ | 29/63 [00:21<00:18, 1.82it/s]
+ 11/29 16G 0.0274 0.01111 0 68 1280: 54%|█████■| 34/63 [00:30<00:23, 1.24it/s]
+ 11/29 16G 0.02742 0.01112 0 69 1280: 56%|█████▌ | 35/63 [00:31<00:24, 1.13it/s]
+ 11/29 16G 0.0273 0.01105 0 64 1280: 62%|██████■| 39/63 [00:32<00:10, 2.24it/s]
+ 11/29 16G 0.02715 0.01102 0 60 1280: 67%|██████▋ | 42/63 [00:35<00:11, 1.81it/s]
+ 11/29 16G 0.02709 0.01106 0 76 1280: 71%|███████■| 45/63 [00:37<00:10, 1.72it/s]
+ 11/29 16G 0.02701 0.01104 0 63 1280: 79%|███████▉ | 50/63 [00:46<00:10, 1.25it/s]
+ 11/29 16G 0.02696 0.01096 0 62 1280: 83%|████████▎ | 52/63 [00:48<00:10, 1.08it/s]
+ 11/29 16G 0.02702 0.01095 0 49 1280: 87%|████████▋ | 55/63 [00:48<00:03, 2.31it/s]
+ 11/29 16G 0.02698 0.01085 0 52 1280: 92%|█████████â–| 58/63 [00:51<00:03, 1.62it/s]
+ 11/29 16G 0.02688 0.01083 0 62 1280: 97%|█████████▋| 61/63 [00:53<00:00, 2.04it/s]
+ 11/29 16G 0.0269 0.01084 0 68 1280: 98%|█████████▊| 62/63 [01:08<00:04, 4.83s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 11/29 16G 0.02689 0.01086 0 40 1280: 100%|██████████| 63/63 [01:10<00:00, 1.13s/it]
+ 12/29 16G 0.02612 0.01053 0 68 1280: 44%|████■| 28/63 [00:28<00:20, 1.67it/s]
+ 12/29 16G 0.02607 0.01052 0 71 1280: 46%|████▌ | 29/63 [00:29<00:24, 1.38it/s]
+ 12/29 16G 0.02595 0.01043 0 53 1280: 52%|█████■| 33/63 [00:33<00:21, 1.36it/s]
+ 12/29 16G 0.02595 0.01052 0 79 1280: 54%|█████■| 34/63 [00:33<00:18, 1.55it/s]
+ 12/29 16G 0.02602 0.01039 0 30 1280: 57%|█████▋ | 36/63 [00:37<00:32, 1.21s/it]
+ 12/29 16G 0.02595 0.01043 0 74 1280: 59%|█████▊ | 37/63 [00:40<00:42, 1.62s/it]
+ 12/29 16G 0.02589 0.01043 0 61 1280: 60%|██████ | 38/63 [00:44<01:01, 2.46s/it]
+ 12/29 16G 0.0259 0.01048 0 75 1280: 62%|██████■| 39/63 [00:50<01:23, 3.50s/it]
+ 12/29 16G 0.02585 0.01051 0 62 1280: 68%|██████▊ | 43/63 [00:53<00:25, 1.26s/it]
+ 12/29 16G 0.02578 0.01041 0 63 1280: 76%|███████▌ | 48/63 [00:56<00:10, 1.48it/s]
+ 12/29 16G 0.0258 0.0104 0 51 1280: 78%|███████▊ | 49/63 [00:57<00:10, 1.40it/s]
+ 12/29 16G 0.02581 0.01028 0 45 1280: 87%|████████▋ | 55/63 [01:00<00:03, 2.38it/s]
+ 12/29 16G 0.02584 0.01031 0 77 1280: 89%|████████▉ | 56/63 [01:02<00:06, 1.13it/s]
+ 12/29 16G 0.02592 0.0105 0 72 1280: 95%|█████████▌| 60/63 [01:04<00:01, 1.92it/s]
+ 12/29 16G 0.02589 0.01056 0 44 1280: 100%|██████████| 63/63 [01:05<00:00, 1.04s/it]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.78it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.78it/
+ 13/29 16G 0.02321 0.01089 0 83 1280: 10%|â–‰ | 6/63 [00:03<00:24, 2.35it/s] 2.78it/
+ 13/29 16G 0.0248 0.01069 0 57 1280: 19%|█▉ | 12/63 [00:04<00:12, 4.23it/s]2.78it/
+ 13/29 16G 0.02446 0.01058 0 55 1280: 21%|██ | 13/63 [00:07<00:53, 1.08s/it]2.78it/
+ 13/29 16G 0.0247 0.01041 0 68 1280: 25%|██▌ | 16/63 [00:08<00:28, 1.63it/s]2.78it/
+ 13/29 16G 0.02473 0.01037 0 57 1280: 30%|███ | 19/63 [00:10<00:23, 1.85it/s]2.78it/
+ 13/29 16G 0.0247 0.01046 0 78 1280: 32%|███■| 20/63 [00:14<01:08, 1.58s/it]2.78it/
+ 13/29 16G 0.02482 0.01035 0 46 1280: 38%|███▊ | 24/63 [00:17<00:31, 1.26it/s]2.78it/
+ 13/29 16G 0.02468 0.01039 0 77 1280: 40%|███▉ | 25/63 [00:17<00:25, 1.50it/s]2.78it/
+ 13/29 16G 0.02482 0.01038 0 73 1280: 44%|████■| 28/63 [00:21<00:28, 1.22it/s]2.78it/
+ 13/29 16G 0.02473 0.01011 0 41 1280: 51%|█████ | 32/63 [00:25<00:27, 1.14it/s]2.78it/
+ 13/29 16G 0.02471 0.01024 0 63 1280: 56%|█████▌ | 35/63 [00:29<00:24, 1.13it/s]2.78it/
+ 13/29 16G 0.02473 0.01014 0 43 1280: 57%|█████▋ | 36/63 [00:30<00:25, 1.06it/s]2.78it/
+ 13/29 16G 0.02487 0.01013 0 52 1280: 59%|█████▊ | 37/63 [00:33<00:39, 1.52s/it]2.78it/
+ 13/29 16G 0.02486 0.01029 0 70 1280: 65%|██████▌ | 41/63 [00:35<00:15, 1.43it/s]2.78it/
+ 13/29 16G 0.02488 0.01031 0 88 1280: 70%|██████▉ | 44/63 [00:37<00:12, 1.55it/s]2.78it/
+ 13/29 16G 0.02482 0.01039 0 55 1280: 76%|███████▌ | 48/63 [00:43<00:11, 1.31it/s]2.78it/
+ 13/29 16G 0.02471 0.01041 0 81 1280: 79%|███████▉ | 50/63 [00:46<00:16, 1.23s/it]2.78it/
+ 13/29 16G 0.0246 0.01029 0 47 1280: 84%|████████■| 53/63 [00:49<00:10, 1.02s/it]2.78it/
+ 13/29 16G 0.02471 0.01025 0 65 1280: 92%|█████████â–| 58/63 [00:53<00:03, 1.44it/s]2.78it/
+ 13/29 16G 0.02457 0.01023 0 73 1280: 95%|█████████▌| 60/63 [00:54<00:01, 1.93it/s]2.78it/
+ 13/29 16G 0.0245 0.01029 0 43 1280: 100%|██████████| 63/63 [00:59<00:00, 1.06it/s]2.78it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.41it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.41it/
+ 14/29 16G 0.02505 0.01031 0 67 1280: 10%|â–‰ | 6/63 [00:03<00:19, 2.90it/s] 3.41it/
+ 14/29 16G 0.02403 0.01023 0 69 1280: 14%|█■| 9/63 [00:07<00:46, 1.16it/s] 3.41it/
+ 14/29 16G 0.02395 0.01043 0 80 1280: 21%|██ | 13/63 [00:08<00:22, 2.26it/s]3.41it/
+ 14/29 16G 0.02397 0.0105 0 71 1280: 25%|██▌ | 16/63 [00:13<00:43, 1.09it/s]3.41it/
+ 14/29 16G 0.02397 0.01053 0 70 1280: 27%|██▋ | 17/63 [00:13<00:32, 1.43it/s]3.41it/
+ 14/29 16G 0.024 0.0105 0 70 1280: 32%|███■| 20/63 [00:16<00:28, 1.51it/s]3.41it/
+ 14/29 16G 0.0241 0.0106 0 71 1280: 33%|███▎ | 21/63 [00:19<01:00, 1.44s/it]3.41it/
+ 14/29 16G 0.02414 0.01055 0 59 1280: 35%|███■| 22/63 [00:20<00:53, 1.30s/it]3.41it/
+ 14/29 16G 0.02374 0.01046 0 64 1280: 41%|████■| 26/63 [00:23<00:25, 1.47it/s]3.41it/
+ 14/29 16G 0.02388 0.01042 0 46 1280: 46%|████▌ | 29/63 [00:26<00:24, 1.39it/s]3.41it/
+ 14/29 16G 0.02391 0.01031 0 67 1280: 52%|█████■| 33/63 [00:29<00:16, 1.83it/s]3.41it/
+ 14/29 16G 0.02372 0.01026 0 62 1280: 59%|█████▊ | 37/63 [00:35<00:22, 1.14it/s]3.41it/
+ 14/29 16G 0.02376 0.01028 0 67 1280: 60%|██████ | 38/63 [00:37<00:29, 1.20s/it]3.41it/
+ 14/29 16G 0.02381 0.01024 0 53 1280: 62%|██████■| 39/63 [00:50<01:57, 4.90s/it]3.41it/
+ 14/29 16G 0.02378 0.01019 0 55 1280: 63%|██████▎ | 40/63 [00:58<02:16, 5.92s/it]3.41it/
+ 14/29 16G 0.02372 0.01019 0 72 1280: 65%|██████▌ | 41/63 [01:04<02:05, 5.71s/it]3.41it/
+ 14/29 16G 0.02363 0.01013 0 47 1280: 67%|██████▋ | 42/63 [01:13<02:24, 6.88s/it]3.41it/
+ 14/29 16G 0.02357 0.0101 0 60 1280: 68%|██████▊ | 43/63 [01:19<02:11, 6.58s/it]3.41it/
+ 14/29 16G 0.02359 0.01018 0 87 1280: 70%|██████▉ | 44/63 [01:26<02:07, 6.69s/it]3.41it/
+ 14/29 16G 0.02359 0.01022 0 74 1280: 71%|███████■| 45/63 [01:33<02:03, 6.85s/it]3.41it/
+ 14/29 16G 0.02358 0.01022 0 65 1280: 73%|███████▎ | 46/63 [01:39<01:50, 6.48s/it]3.41it/
+ 14/29 16G 0.02359 0.01026 0 81 1280: 75%|███████■| 47/63 [01:44<01:35, 5.99s/it]3.41it/
+ 14/29 16G 0.02356 0.01021 0 51 1280: 76%|███████▌ | 48/63 [01:48<01:20, 5.37s/it]3.41it/
+ 14/29 16G 0.0235 0.0102 0 55 1280: 78%|███████▊ | 49/63 [01:55<01:22, 5.86s/it]3.41it/
+ 14/29 16G 0.02352 0.01021 0 68 1280: 79%|███████▉ | 50/63 [01:59<01:10, 5.39s/it]3.41it/
+ 14/29 16G 0.02344 0.01021 0 64 1280: 81%|████████ | 51/63 [02:04<01:04, 5.40s/it]3.41it/
+ 14/29 16G 0.02344 0.0102 0 63 1280: 83%|████████▎ | 52/63 [02:09<00:56, 5.10s/it]3.41it/
+ 14/29 16G 0.02345 0.01018 0 60 1280: 84%|████████■| 53/63 [02:11<00:42, 4.25s/it]3.41it/
+ 14/29 16G 0.02348 0.01023 0 59 1280: 87%|████████▋ | 55/63 [02:13<00:20, 2.62s/it]3.41it/
+ 14/29 16G 0.02347 0.01023 0 64 1280: 89%|████████▉ | 56/63 [02:17<00:21, 3.06s/it]3.41it/
+ 14/29 16G 0.02347 0.01022 0 68 1280: 90%|█████████ | 57/63 [02:24<00:24, 4.12s/it]3.41it/
+ 14/29 16G 0.02344 0.01028 0 89 1280: 92%|█████████â–| 58/63 [02:28<00:21, 4.20s/it]3.41it/
+ 14/29 16G 0.02343 0.01034 0 52 1280: 98%|█████████▊| 62/63 [02:32<00:01, 1.49s/it]3.41it/
+ 14/29 16G 0.02345 0.01038 0 40 1280: 100%|██████████| 63/63 [02:33<00:00, 2.43s/it]3.41it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.62it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.62it/
+ 15/29 16G 0.02209 0.009758 0 65 1280: 6%|â–‹ | 4/63 [00:04<00:52, 1.11it/s] 2.62it/
+ 15/29 16G 0.02187 0.01007 0 80 1280: 8%|â–Š | 5/63 [00:06<01:02, 1.08s/it] 2.62it/
+ 15/29 16G 0.02214 0.01061 0 60 1280: 13%|█▎ | 8/63 [00:08<00:39, 1.41it/s] 2.62it/
+ 15/29 16G 0.02156 0.009993 0 43 1280: 19%|█▉ | 12/63 [00:10<00:26, 1.96it/s]2.62it/
+ 15/29 16G 0.02107 0.01002 0 66 1280: 35%|███■| 22/63 [00:12<00:08, 4.70it/s]2.62it/
+ 15/29 16G 0.02109 0.009933 0 72 1280: 43%|████▎ | 27/63 [00:14<00:11, 3.18it/s]2.62it/
+ 15/29 16G 0.02106 0.01002 0 94 1280: 51%|█████ | 32/63 [00:15<00:06, 4.69it/s]2.62it/
+ 15/29 16G 0.02103 0.01003 0 72 1280: 52%|█████■| 33/63 [00:18<00:30, 1.00s/it]2.62it/
+ 15/29 16G 0.02102 0.009965 0 51 1280: 54%|█████■| 34/63 [00:20<00:39, 1.37s/it]2.62it/
+ 15/29 16G 0.02101 0.01 0 61 1280: 59%|█████▊ | 37/63 [00:21<00:19, 1.31it/s]2.62it/
+ 15/29 16G 0.02098 0.009956 0 56 1280: 62%|██████■| 39/63 [00:24<00:22, 1.08it/s]2.62it/
+ 15/29 16G 0.02084 0.009905 0 55 1280: 68%|██████▊ | 43/63 [00:26<00:13, 1.51it/s]2.62it/
+ 15/29 16G 0.02082 0.009925 0 76 1280: 70%|██████▉ | 44/63 [00:26<00:10, 1.89it/s]2.62it/
+ 15/29 16G 0.02084 0.009811 0 46 1280: 73%|███████▎ | 46/63 [00:30<00:18, 1.10s/it]2.62it/
+ 15/29 16G 0.02087 0.009733 0 54 1280: 76%|███████▌ | 48/63 [00:31<00:09, 1.55it/s]2.62it/
+ 15/29 16G 0.02085 0.009722 0 65 1280: 78%|███████▊ | 49/63 [00:33<00:18, 1.32s/it]2.62it/
+ 15/29 16G 0.02091 0.009689 0 56 1280: 84%|████████■| 53/63 [00:38<00:09, 1.04it/s]2.62it/
+ 15/29 16G 0.02089 0.009696 0 87 1280: 89%|████████▉ | 56/63 [00:40<00:05, 1.32it/s]2.62it/
+ 15/29 16G 0.02095 0.009725 0 75 1280: 92%|█████████â–| 58/63 [00:41<00:03, 1.60it/s]2.62it/
+ 15/29 16G 0.02097 0.009696 0 57 1280: 95%|█████████▌| 60/63 [00:43<00:02, 1.40it/s]2.62it/
+ 15/29 16G 0.02096 0.009698 0 71 1280: 97%|█████████▋| 61/63 [00:46<00:03, 1.53s/it]2.62it/
+ 15/29 16G 0.02109 0.009671 0 26 1280: 100%|██████████| 63/63 [00:47<00:00, 1.33it/s]2.62it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.88it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.88it/
+ 16/29 16G 0.0208 0.009093 0 64 1280: 11%|â–ˆ | 7/63 [00:05<00:38, 1.45it/s] 3.88it/
+ 16/29 16G 0.02126 0.009342 0 70 1280: 17%|█▋ | 11/63 [00:09<00:34, 1.49it/s]3.88it/
+ 16/29 16G 0.02066 0.009322 0 52 1280: 21%|██ | 13/63 [00:09<00:25, 2.00it/s]3.88it/
+ 16/29 16G 0.02091 0.009311 0 61 1280: 22%|██■| 14/63 [00:12<00:49, 1.01s/it]3.88it/
+ 16/29 16G 0.02131 0.009391 0 80 1280: 27%|██▋ | 17/63 [00:14<00:33, 1.38it/s]3.88it/
+ 16/29 16G 0.02115 0.009353 0 61 1280: 29%|██▊ | 18/63 [00:16<00:56, 1.26s/it]3.88it/
+ 16/29 16G 0.02083 0.009212 0 65 1280: 35%|███■| 22/63 [00:21<00:35, 1.16it/s]3.88it/
+ 16/29 16G 0.02082 0.009252 0 54 1280: 37%|███▋ | 23/63 [00:22<00:40, 1.01s/it]3.88it/
+ 16/29 16G 0.02088 0.009337 0 63 1280: 43%|████▎ | 27/63 [00:24<00:18, 1.91it/s]3.88it/
+ 16/29 16G 0.02073 0.009249 0 51 1280: 44%|████■| 28/63 [00:27<00:41, 1.18s/it]3.88it/
+ 16/29 16G 0.02074 0.009159 0 62 1280: 48%|████▊ | 30/63 [00:28<00:28, 1.16it/s]3.88it/
+ 16/29 16G 0.02079 0.009215 0 42 1280: 52%|█████■| 33/63 [00:30<00:19, 1.53it/s]3.88it/
+ 16/29 16G 0.02076 0.009211 0 65 1280: 54%|█████■| 34/63 [00:34<00:52, 1.82s/it]3.88it/
+ 16/29 16G 0.02076 0.009152 0 46 1280: 56%|█████▌ | 35/63 [00:36<00:51, 1.83s/it]3.88it/
+ 16/29 16G 0.02073 0.00933 0 64 1280: 62%|██████■| 39/63 [00:38<00:18, 1.33it/s]3.88it/
+ 16/29 16G 0.02075 0.009359 0 77 1280: 65%|██████▌ | 41/63 [00:41<00:21, 1.04it/s]3.88it/
+ 16/29 16G 0.02079 0.009269 0 55 1280: 71%|███████■| 45/63 [00:42<00:10, 1.77it/s]3.88it/
+ 16/29 16G 0.02078 0.009272 0 72 1280: 73%|███████▎ | 46/63 [00:44<00:13, 1.29it/s]3.88it/
+ 16/29 16G 0.0207 0.009357 0 96 1280: 78%|███████▊ | 49/63 [00:47<00:10, 1.30it/s]3.88it/
+ 16/29 16G 0.02073 0.009406 0 83 1280: 79%|███████▉ | 50/63 [00:49<00:17, 1.33s/it]3.88it/
+ 16/29 16G 0.02078 0.009351 0 41 1280: 84%|████████■| 53/63 [00:53<00:11, 1.10s/it]3.88it/
+ 16/29 16G 0.02071 0.009314 0 67 1280: 87%|████████▋ | 55/63 [00:54<00:06, 1.25it/s]3.88it/
+ 16/29 16G 0.02079 0.009383 0 79 1280: 94%|█████████▎| 59/63 [00:56<00:02, 1.97it/s]3.88it/
+ 16/29 16G 0.02086 0.009393 0 55 1280: 97%|█████████▋| 61/63 [00:59<00:01, 1.27it/s]3.88it/
+ 16/29 16G 0.02088 0.009402 0 34 1280: 100%|██████████| 63/63 [01:01<00:00, 1.02it/s]3.88it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.59it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.59it/
+ 17/29 16G 0.01874 0.009063 0 78 1280: 13%|█▎ | 8/63 [00:06<00:34, 1.61it/s] 3.59it/
+ 17/29 16G 0.01879 0.009079 0 70 1280: 19%|█▉ | 12/63 [00:07<00:20, 2.50it/s]3.59it/
+ 17/29 16G 0.01861 0.008832 0 55 1280: 22%|██■| 14/63 [00:09<00:31, 1.54it/s]3.59it/
+ 17/29 16G 0.0191 0.008973 0 66 1280: 29%|██▊ | 18/63 [00:13<00:27, 1.66it/s]3.59it/
+ 17/29 16G 0.01919 0.008932 0 56 1280: 32%|███■| 20/63 [00:22<01:38, 2.29s/it]3.59it/
+ 17/29 16G 0.01948 0.009062 0 61 1280: 38%|███▊ | 24/63 [00:24<00:30, 1.29it/s]3.59it/
+ 17/29 16G 0.01938 0.008944 0 61 1280: 43%|████▎ | 27/63 [00:29<00:40, 1.12s/it]3.59it/
+ 17/29 16G 0.01926 0.008885 0 64 1280: 51%|█████ | 32/63 [00:31<00:18, 1.63it/s]3.59it/
+ 17/29 16G 0.01934 0.008886 0 63 1280: 54%|█████■| 34/63 [00:32<00:19, 1.46it/s]3.59it/
+ 17/29 16G 0.01927 0.008822 0 71 1280: 60%|██████ | 38/63 [00:41<00:27, 1.08s/it]3.59it/
+ 17/29 16G 0.01926 0.008905 0 62 1280: 63%|██████▎ | 40/63 [00:42<00:19, 1.17it/s]3.59it/
+ 17/29 16G 0.01923 0.008873 0 57 1280: 65%|██████▌ | 41/63 [00:50<01:09, 3.17s/it]3.59it/
+ 17/29 16G 0.01923 0.008822 0 45 1280: 75%|███████■| 47/63 [00:54<00:11, 1.34it/s]3.59it/
+ 17/29 16G 0.01911 0.008799 0 61 1280: 79%|███████▉ | 50/63 [00:56<00:06, 1.95it/s]3.59it/
+ 17/29 16G 0.01916 0.008826 0 80 1280: 81%|████████ | 51/63 [01:03<00:29, 2.45s/it]3.59it/
+ 17/29 16G 0.01913 0.008812 0 69 1280: 86%|████████▌ | 54/63 [01:05<00:11, 1.23s/it]3.59it/
+ 17/29 16G 0.01918 0.008919 0 54 1280: 92%|█████████â–| 58/63 [01:07<00:03, 1.58it/s]3.59it/
+ 17/29 16G 0.01922 0.008897 0 51 1280: 94%|█████████▎| 59/63 [01:10<00:05, 1.32s/it]3.59it/
+ 17/29 16G 0.01926 0.008908 0 67 1280: 98%|█████████▊| 62/63 [01:15<00:01, 1.36s/it]3.59it/
+ 17/29 16G 0.01927 0.008899 0 31 1280: 100%|██████████| 63/63 [01:16<00:00, 1.21s/it]3.59it/
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:02<00:08, 1.43s/i
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:05<00:10, 2.01s/i
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:07<00:01, 1.09it/
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:09<00:01, 1.19s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:10<00:00, 1.35s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:10<00:00, 1.35s/i
+ 18/29 16G 0.02293 0.01038 0 70 1280: 2%|â– | 1/63 [00:03<03:21, 3.25s/it] 1.35s/i
+ 18/29 16G 0.02147 0.009179 0 56 1280: 3%|â–Ž | 2/63 [00:08<04:14, 4.17s/it] 1.35s/i
+ 18/29 16G 0.0195 0.008506 0 67 1280: 8%|â–Š | 5/63 [00:14<02:01, 2.10s/it] 1.35s/i
+ 18/29 16G 0.01843 0.008233 0 59 1280: 11%|â–ˆ | 7/63 [00:14<01:05, 1.17s/it] 1.35s/i
+ 18/29 16G 0.01882 0.00874 0 61 1280: 14%|█■| 9/63 [00:18<01:12, 1.35s/it] 1.35s/i
+ 18/29 16G 0.01891 0.008737 0 62 1280: 17%|█▋ | 11/63 [00:20<01:04, 1.24s/it]1.35s/i
+ 18/29 16G 0.01889 0.008666 0 60 1280: 21%|██ | 13/63 [00:22<00:51, 1.02s/it]1.35s/i
+ 18/29 16G 0.01895 0.009119 0 71 1280: 30%|███ | 19/63 [00:24<00:20, 2.16it/s]1.35s/i
+ 18/29 16G 0.01914 0.009082 0 57 1280: 32%|███■| 20/63 [00:25<00:32, 1.34it/s]1.35s/i
+ 18/29 16G 0.01923 0.009105 0 60 1280: 33%|███▎ | 21/63 [00:28<00:50, 1.20s/it]1.35s/i
+ 18/29 16G 0.0194 0.009033 0 52 1280: 37%|███▋ | 23/63 [00:30<00:44, 1.12s/it]1.35s/i
+ 18/29 16G 0.01918 0.008954 0 59 1280: 41%|████■| 26/63 [00:32<00:32, 1.15it/s]1.35s/i
+ 18/29 16G 0.01917 0.008977 0 77 1280: 43%|████▎ | 27/63 [00:33<00:26, 1.36it/s]1.35s/i
+ 18/29 16G 0.01928 0.009011 0 79 1280: 48%|████▊ | 30/63 [00:35<00:25, 1.29it/s]1.35s/i
+ 18/29 16G 0.01923 0.009027 0 87 1280: 51%|█████ | 32/63 [00:38<00:30, 1.02it/s]1.35s/i
+ 18/29 16G 0.01918 0.008981 0 59 1280: 52%|█████■| 33/63 [00:39<00:33, 1.13s/it]1.35s/i
+ 18/29 16G 0.01913 0.008941 0 61 1280: 56%|█████▌ | 35/63 [00:41<00:29, 1.05s/it]1.35s/i
+ 18/29 16G 0.01917 0.008925 0 50 1280: 57%|█████▋ | 36/63 [00:44<00:43, 1.61s/it]1.35s/i
+ 18/29 16G 0.01912 0.008915 0 57 1280: 62%|██████■| 39/63 [00:46<00:26, 1.10s/it]1.35s/i
+ 18/29 16G 0.01907 0.008925 0 58 1280: 65%|██████▌ | 41/63 [00:49<00:25, 1.16s/it]1.35s/i
+ 18/29 16G 0.01902 0.009021 0 67 1280: 70%|██████▉ | 44/63 [00:51<00:15, 1.20it/s]1.35s/i
+ 18/29 16G 0.019 0.009052 0 88 1280: 75%|███████■| 47/63 [00:52<00:08, 1.82it/s]1.35s/i
+ 18/29 16G 0.01914 0.009143 0 97 1280: 78%|███████▊ | 49/63 [00:54<00:10, 1.34it/s]1.35s/i
+ 18/29 16G 0.01915 0.009214 0 79 1280: 81%|████████ | 51/63 [00:57<00:11, 1.02it/s]1.35s/i
+ 18/29 16G 0.01909 0.009165 0 45 1280: 83%|████████▎ | 52/63 [00:59<00:14, 1.27s/it]1.35s/i
+ 18/29 16G 0.01905 0.009153 0 61 1280: 84%|████████■| 53/63 [01:01<00:15, 1.56s/it]1.35s/i
+ 18/29 16G 0.01902 0.009216 0 85 1280: 87%|████████▋ | 55/63 [01:03<00:09, 1.18s/it]1.35s/i
+ 18/29 16G 0.01904 0.009236 0 85 1280: 89%|████████▉ | 56/63 [01:04<00:07, 1.12s/it]1.35s/i
+ 18/29 16G 0.01903 0.009196 0 69 1280: 92%|█████████â–| 58/63 [01:10<00:09, 1.91s/it]1.35s/i
+ 18/29 16G 0.01906 0.009194 0 61 1280: 94%|█████████▎| 59/63 [01:12<00:08, 2.19s/it]1.35s/i
+ 18/29 16G 0.01907 0.009148 0 47 1280: 95%|█████████▌| 60/63 [01:15<00:06, 2.20s/it]1.35s/i
+ 18/29 16G 0.01909 0.009157 0 64 1280: 97%|█████████▋| 61/63 [01:17<00:04, 2.32s/it]1.35s/i
+ 18/29 16G 0.0191 0.009148 0 69 1280: 98%|█████████▊| 62/63 [01:23<00:03, 3.41s/it]1.35s/i
+ 18/29 16G 0.01906 0.00911 0 29 1280: 100%|██████████| 63/63 [01:24<00:00, 1.35s/it]1.35s/i
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:03<00:01, 1.76it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.77it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.77it/
+ 19/29 16G 0.01849 0.00794 0 70 1280: 3%|â–Ž | 2/63 [00:03<01:43, 1.69s/it] 1.77it/
+ 19/29 16G 0.0176 0.008228 0 75 1280: 6%|â–‹ | 4/63 [00:05<00:59, 1.00s/it] 1.77it/
+ 19/29 16G 0.01842 0.008576 0 66 1280: 11%|â–ˆ | 7/63 [00:08<00:48, 1.16it/s] 1.77it/
+ 19/29 16G 0.01829 0.009205 0 106 1280: 16%|█▌ | 10/63 [00:10<00:35, 1.49it/s]1.77it/
+ 19/29 16G 0.01812 0.009178 0 69 1280: 17%|█▋ | 11/63 [00:12<00:58, 1.12s/it]1.77it/
+ 19/29 16G 0.01812 0.009204 0 91 1280: 24%|██■| 15/63 [00:14<00:32, 1.49it/s]1.77it/
+ 19/29 16G 0.01816 0.009088 0 54 1280: 25%|██▌ | 16/63 [00:17<00:59, 1.26s/it]1.77it/
+ 19/29 16G 0.01811 0.009038 0 61 1280: 27%|██▋ | 17/63 [00:21<01:36, 2.10s/it]1.77it/
+ 19/29 16G 0.01785 0.009025 0 67 1280: 33%|███▎ | 21/63 [00:27<00:53, 1.26s/it]1.77it/
+ 19/29 16G 0.01798 0.009148 0 79 1280: 38%|███▊ | 24/63 [00:28<00:33, 1.18it/s]1.77it/
+ 19/29 16G 0.018 0.009159 0 83 1280: 41%|████■| 26/63 [00:30<00:32, 1.13it/s]1.77it/
+ 19/29 16G 0.01787 0.009243 0 73 1280: 48%|████▊ | 30/63 [00:32<00:20, 1.63it/s]1.77it/
+ 19/29 16G 0.01797 0.009364 0 105 1280: 51%|█████ | 32/63 [00:33<00:15, 1.99it/s]1.77it/
+ 19/29 16G 0.01791 0.009249 0 63 1280: 57%|█████▋ | 36/63 [00:36<00:15, 1.75it/s]1.77it/
+ 19/29 16G 0.01789 0.009242 0 58 1280: 62%|██████■| 39/63 [00:38<00:12, 1.86it/s]1.77it/
+ 19/29 16G 0.01781 0.009178 0 58 1280: 70%|██████▉ | 44/63 [00:40<00:08, 2.28it/s]1.77it/
+ 19/29 16G 0.01783 0.009081 0 69 1280: 78%|███████▊ | 49/63 [00:42<00:05, 2.78it/s]1.77it/
+ 19/29 16G 0.01774 0.009107 0 80 1280: 83%|████████▎ | 52/63 [00:44<00:05, 1.85it/s]1.77it/
+ 19/29 16G 0.01775 0.009043 0 64 1280: 86%|████████▌ | 54/63 [00:46<00:06, 1.45it/s]1.77it/
+ 19/29 16G 0.01776 0.008979 0 70 1280: 89%|████████▉ | 56/63 [00:48<00:05, 1.21it/s]1.77it/
+ 19/29 16G 0.01781 0.008976 0 55 1280: 95%|█████████▌| 60/63 [00:50<00:02, 1.45it/s]1.77it/
+ 19/29 16G 0.0178 0.008958 0 76 1280: 98%|█████████▊| 62/63 [00:53<00:00, 1.21it/s]1.77it/
+ 19/29 16G 0.01783 0.008928 0 27 1280: 100%|██████████| 63/63 [00:53<00:00, 1.18it/s]1.77it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.20it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.20it/
+ 20/29 16G 0.01555 0.008185 0 68 1280: 5%|â– | 3/63 [00:04<01:34, 1.58s/it] 3.20it/
+ 20/29 16G 0.01663 0.008138 0 72 1280: 10%|â–‰ | 6/63 [00:06<00:46, 1.24it/s] 3.20it/
+ 20/29 16G 0.01775 0.008053 0 54 1280: 11%|â–ˆ | 7/63 [00:06<00:41, 1.36it/s] 3.20it/
+ 20/29 16G 0.01736 0.008468 0 52 1280: 21%|██ | 13/63 [00:10<00:22, 2.23it/s]3.20it/
+ 20/29 16G 0.01753 0.008763 0 60 1280: 27%|██▋ | 17/63 [00:14<00:27, 1.68it/s]3.20it/
+ 20/29 16G 0.01732 0.008655 0 52 1280: 29%|██▊ | 18/63 [00:18<01:06, 1.47s/it]3.20it/
+ 20/29 16G 0.01713 0.008594 0 50 1280: 30%|███ | 19/63 [00:20<01:19, 1.80s/it]3.20it/
+ 20/29 16G 0.01703 0.008448 0 50 1280: 32%|███■| 20/63 [00:21<00:57, 1.33s/it]3.20it/
+ 20/29 16G 0.01706 0.008415 0 58 1280: 37%|███▋ | 23/63 [00:23<00:34, 1.16it/s]3.20it/
+ 20/29 16G 0.01718 0.008362 0 62 1280: 40%|███▉ | 25/63 [00:26<00:46, 1.22s/it]3.20it/
+ 20/29 16G 0.01671 0.008267 0 46 1280: 46%|████▌ | 29/63 [00:27<00:15, 2.23it/s]3.20it/
+ 20/29 16G 0.01671 0.008254 0 55 1280: 52%|█████■| 33/63 [00:30<00:13, 2.25it/s]3.20it/
+ 20/29 16G 0.01668 0.008261 0 69 1280: 54%|█████■| 34/63 [00:33<00:34, 1.18s/it]3.20it/
+ 20/29 16G 0.01665 0.008323 0 72 1280: 59%|█████▊ | 37/63 [00:38<00:34, 1.31s/it]3.20it/
+ 20/29 16G 0.01674 0.008408 0 78 1280: 62%|██████■| 39/63 [00:39<00:18, 1.32it/s]3.20it/
+ 20/29 16G 0.01682 0.008391 0 73 1280: 65%|██████▌ | 41/63 [00:42<00:24, 1.13s/it]3.20it/
+ 20/29 16G 0.01667 0.008258 0 54 1280: 71%|███████■| 45/63 [00:43<00:07, 2.33it/s]3.20it/
+ 20/29 16G 0.01673 0.008248 0 61 1280: 73%|███████▎ | 46/63 [00:46<00:21, 1.29s/it]3.20it/
+ 20/29 16G 0.01682 0.00827 0 81 1280: 78%|███████▊ | 49/63 [00:47<00:08, 1.71it/s]3.20it/
+ 20/29 16G 0.01681 0.00829 0 74 1280: 79%|███████▉ | 50/63 [00:49<00:11, 1.16it/s]3.20it/
+ 20/29 16G 0.01673 0.008143 0 44 1280: 86%|████████▌ | 54/63 [00:55<00:08, 1.02it/s]3.20it/
+ 20/29 16G 0.01674 0.008147 0 66 1280: 87%|████████▋ | 55/63 [00:55<00:05, 1.35it/s]3.20it/
+ 20/29 16G 0.01672 0.008141 0 66 1280: 94%|█████████▎| 59/63 [00:58<00:02, 1.46it/s]3.20it/
+ 20/29 16G 0.01675 0.008156 0 75 1280: 97%|█████████▋| 61/63 [00:59<00:01, 1.98it/s]3.20it/
+ 20/29 16G 0.01673 0.008199 0 51 1280: 100%|██████████| 63/63 [01:02<00:00, 1.01it/s]3.20it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.07it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.07it/
+ 21/29 16G 0.01471 0.008807 0 70 1280: 6%|â–‹ | 4/63 [00:03<01:14, 1.27s/it] 4.07it/
+ 21/29 16G 0.01495 0.008671 0 55 1280: 11%|â–ˆ | 7/63 [00:05<00:50, 1.11it/s] 4.07it/
+ 21/29 16G 0.01517 0.008721 0 78 1280: 13%|█▎ | 8/63 [00:06<00:36, 1.49it/s] 4.07it/
+ 21/29 16G 0.01509 0.008521 0 46 1280: 22%|██■| 14/63 [00:09<00:21, 2.30it/s]4.07it/
+ 21/29 16G 0.0153 0.008437 0 61 1280: 29%|██▊ | 18/63 [00:12<00:21, 2.10it/s]4.07it/
+ 21/29 16G 0.01524 0.00839 0 70 1280: 30%|███ | 19/63 [00:14<00:42, 1.04it/s]4.07it/
+ 21/29 16G 0.01515 0.008346 0 59 1280: 32%|███■| 20/63 [00:24<02:33, 3.58s/it]4.07it/
+ 21/29 16G 0.0151 0.008365 0 69 1280: 33%|███▎ | 21/63 [00:27<02:17, 3.27s/it]4.07it/
+ 21/29 16G 0.01523 0.008539 0 85 1280: 44%|████■| 28/63 [00:29<00:19, 1.83it/s]4.07it/
+ 21/29 16G 0.01517 0.00856 0 68 1280: 46%|████▌ | 29/63 [00:32<00:34, 1.02s/it]4.07it/
+ 21/29 16G 0.0152 0.008399 0 59 1280: 54%|█████■| 34/63 [00:34<00:14, 2.02it/s]4.07it/
+ 21/29 16G 0.01521 0.008388 0 67 1280: 56%|█████▌ | 35/63 [00:34<00:14, 1.87it/s]4.07it/
+ 21/29 16G 0.01517 0.008302 0 58 1280: 62%|██████■| 39/63 [00:42<00:25, 1.04s/it]4.07it/
+ 21/29 16G 0.01523 0.008265 0 66 1280: 67%|██████▋ | 42/63 [00:46<00:22, 1.09s/it]4.07it/
+ 21/29 16G 0.01521 0.008262 0 66 1280: 68%|██████▊ | 43/63 [00:46<00:17, 1.15it/s]4.07it/
+ 21/29 16G 0.01513 0.00821 0 62 1280: 71%|███████■| 45/63 [00:50<00:22, 1.27s/it]4.07it/
+ 21/29 16G 0.0153 0.008212 0 64 1280: 79%|███████▉ | 50/63 [00:52<00:06, 1.86it/s]4.07it/
+ 21/29 16G 0.01529 0.008237 0 76 1280: 81%|████████ | 51/63 [00:53<00:08, 1.36it/s]4.07it/
+ 21/29 16G 0.01523 0.008174 0 63 1280: 84%|████████■| 53/63 [01:02<00:23, 2.31s/it]4.07it/
+
+ 21/29 16G 0.01524 0.008158 0 64 1280: 87%|████████▋ | 55/63 [01:06<00:17, 2.16s/it]4.07it/
+ 21/29 16G 0.01526 0.008198 0 82 1280: 89%|████████▉ | 56/63 [01:09<00:16, 2.41s/it]4.07it/
+ 21/29 16G 0.01529 0.008229 0 68 1280: 92%|█████████â–| 58/63 [01:13<00:10, 2.14s/it]4.07it/
+ 21/29 16G 0.01528 0.008255 0 69 1280: 94%|█████████▎| 59/63 [01:15<00:07, 1.96s/it]4.07it/
+ 21/29 16G 0.01527 0.008269 0 33 1280: 100%|██████████| 63/63 [01:17<00:00, 1.24s/it]4.07it/
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 2.44it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.46it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.46it/
+ 22/29 16G 0.01481 0.00769 0 81 1280: 13%|█▎ | 8/63 [00:03<00:22, 2.41it/s] 2.46it/
+ 22/29 16G 0.01515 0.007317 0 51 1280: 19%|█▉ | 12/63 [00:05<00:21, 2.39it/s]2.46it/
+ 22/29 16G 0.01511 0.007206 0 48 1280: 21%|██ | 13/63 [00:06<00:38, 1.28it/s]2.46it/
+ 22/29 16G 0.01491 0.007174 0 67 1280: 24%|██■| 15/63 [00:09<00:50, 1.06s/it]2.46it/
+ 22/29 16G 0.01509 0.007342 0 71 1280: 27%|██▋ | 17/63 [00:12<00:53, 1.17s/it]2.46it/
+ 22/29 16G 0.01489 0.007362 0 63 1280: 32%|███■| 20/63 [00:14<00:38, 1.11it/s]2.46it/
+ 22/29 16G 0.01483 0.007401 0 76 1280: 33%|███▎ | 21/63 [00:16<00:44, 1.07s/it]2.46it/
+ 22/29 16G 0.01491 0.007327 0 43 1280: 35%|███■| 22/63 [00:17<00:53, 1.31s/it]2.46it/
+ 22/29 16G 0.01505 0.007404 0 59 1280: 38%|███▊ | 24/63 [00:20<00:49, 1.27s/it]2.46it/
+ 22/29 16G 0.015 0.007468 0 77 1280: 44%|████■| 28/63 [00:21<00:19, 1.82it/s]2.46it/
+ 22/29 16G 0.01507 0.007603 0 87 1280: 48%|████▊ | 30/63 [00:23<00:20, 1.63it/s]2.46it/
+ 22/29 16G 0.01503 0.007584 0 59 1280: 49%|████▉ | 31/63 [00:26<00:41, 1.29s/it]2.46it/
+ 22/29 16G 0.01487 0.007541 0 57 1280: 56%|█████▌ | 35/63 [00:28<00:19, 1.44it/s]2.46it/
+ 22/29 16G 0.01489 0.007507 0 58 1280: 57%|█████▋ | 36/63 [00:29<00:19, 1.42it/s]2.46it/
+ 22/29 16G 0.01489 0.007501 0 71 1280: 65%|██████▌ | 41/63 [00:36<00:17, 1.26it/s]2.46it/
+ 22/29 16G 0.01484 0.007497 0 57 1280: 67%|██████▋ | 42/63 [00:37<00:20, 1.04it/s]2.46it/
+ 22/29 16G 0.01485 0.007557 0 69 1280: 70%|██████▉ | 44/63 [00:39<00:16, 1.13it/s]2.46it/
+ 22/29 16G 0.01486 0.007611 0 55 1280: 76%|███████▌ | 48/63 [00:42<00:10, 1.43it/s]2.46it/
+ 22/29 16G 0.01477 0.007639 0 68 1280: 81%|████████ | 51/63 [00:43<00:05, 2.28it/s]2.46it/
+ 22/29 16G 0.01478 0.007641 0 70 1280: 83%|████████▎ | 52/63 [00:46<00:11, 1.03s/it]2.46it/
+ 22/29 16G 0.01478 0.007641 0 73 1280: 86%|████████▌ | 54/63 [00:50<00:13, 1.49s/it]2.46it/
+ 22/29 16G 0.01472 0.007655 0 77 1280: 94%|█████████▎| 59/63 [00:54<00:02, 1.52it/s]2.46it/
+ 22/29 16G 0.01469 0.007655 0 56 1280: 95%|█████████▌| 60/63 [00:54<00:01, 1.95it/s]2.46it/
+ 22/29 16G 0.01473 0.007641 0 59 1280: 97%|█████████▋| 61/63 [01:02<00:05, 2.72s/it]2.46it/
+ 22/29 16G 0.01476 0.007651 0 32 1280: 100%|██████████| 63/63 [01:04<00:00, 1.02s/it]2.46it/
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:02<00:00, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.44it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.44it/
+ 23/29 16G 0.01468 0.007513 0 58 1280: 11%|â–ˆ | 7/63 [00:06<00:34, 1.61it/s] 2.44it/
+ 23/29 16G 0.01465 0.007724 0 87 1280: 13%|█▎ | 8/63 [00:07<00:36, 1.52it/s] 2.44it/
+ 23/29 16G 0.01469 0.007876 0 70 1280: 16%|█▌ | 10/63 [00:10<00:54, 1.03s/it]2.44it/
+ 23/29 16G 0.01472 0.007704 0 54 1280: 21%|██ | 13/63 [00:11<00:33, 1.48it/s]2.44it/
+ 23/29 16G 0.01461 0.007808 0 65 1280: 24%|██■| 15/63 [00:14<00:45, 1.06it/s]2.44it/
+ 23/29 16G 0.01448 0.007754 0 58 1280: 25%|██▌ | 16/63 [00:15<00:51, 1.09s/it]2.44it/
+ 23/29 16G 0.01443 0.007732 0 46 1280: 30%|███ | 19/63 [00:18<00:45, 1.03s/it]2.44it/
+ 23/29 16G 0.01444 0.007794 0 77 1280: 32%|███■| 20/63 [00:22<01:23, 1.95s/it]2.44it/
+ 23/29 16G 0.0144 0.007856 0 79 1280: 33%|███▎ | 21/63 [00:27<01:53, 2.71s/it]2.44it/
+ 23/29 16G 0.01423 0.007857 0 51 1280: 37%|███▋ | 23/63 [00:30<01:26, 2.17s/it]2.44it/
+ 23/29 16G 0.01419 0.007906 0 53 1280: 43%|████▎ | 27/63 [00:32<00:28, 1.28it/s]2.44it/
+ 23/29 16G 0.01422 0.007633 0 46 1280: 49%|████▉ | 31/63 [00:34<00:18, 1.73it/s]2.44it/
+ 23/29 16G 0.01429 0.007737 0 99 1280: 51%|█████ | 32/63 [00:35<00:20, 1.49it/s]2.44it/
+ 23/29 16G 0.0143 0.007692 0 65 1280: 59%|█████▊ | 37/63 [00:41<00:17, 1.50it/s]2.44it/
+ 23/29 16G 0.01427 0.007657 0 63 1280: 63%|██████▎ | 40/63 [00:43<00:15, 1.45it/s]2.44it/
+ 23/29 16G 0.01423 0.00764 0 84 1280: 67%|██████▋ | 42/63 [00:44<00:13, 1.54it/s]2.44it/
+ 23/29 16G 0.01425 0.007621 0 54 1280: 68%|██████▊ | 43/63 [00:46<00:19, 1.04it/s]2.44it/
+ 23/29 16G 0.01424 0.007596 0 57 1280: 70%|██████▉ | 44/63 [00:49<00:30, 1.58s/it]2.44it/
+ 23/29 16G 0.01421 0.007564 0 60 1280: 71%|███████■| 45/63 [00:50<00:27, 1.50s/it]2.44it/
+ 23/29 16G 0.01422 0.00756 0 76 1280: 75%|███████■| 47/63 [00:52<00:18, 1.15s/it]2.44it/
+ 23/29 16G 0.01426 0.007535 0 52 1280: 76%|███████▌ | 48/63 [00:54<00:21, 1.43s/it]2.44it/
+ 23/29 16G 0.01421 0.007633 0 85 1280: 83%|████████▎ | 52/63 [00:56<00:07, 1.38it/s]2.44it/
+ 23/29 16G 0.01419 0.007605 0 56 1280: 86%|████████▌ | 54/63 [00:57<00:05, 1.57it/s]2.44it/
+ 23/29 16G 0.01419 0.007585 0 67 1280: 89%|████████▉ | 56/63 [01:03<00:10, 1.45s/it]2.44it/
+ 23/29 16G 0.01414 0.007548 0 55 1280: 95%|█████████▌| 60/63 [01:04<00:01, 1.51it/s]2.44it/
+ 23/29 16G 0.01414 0.007544 0 61 1280: 97%|█████████▋| 61/63 [01:09<00:03, 1.88s/it]2.44it/
+ 23/29 16G 0.01412 0.007538 0 57 1280: 98%|█████████▊| 62/63 [01:15<00:03, 3.10s/it]2.44it/
+ 23/29 16G 0.01415 0.007514 0 33 1280: 100%|██████████| 63/63 [01:16<00:00, 1.22s/it]2.44it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:02<00:03, 1.32it/
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:03<00:03, 1.15it/
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:05<00:04, 1.35s/i
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:07<00:02, 1.47s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:09<00:00, 1.23s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:09<00:00, 1.23s/i
+ 24/29 16G 0.01311 0.007528 0 64 1280: 3%|â–Ž | 2/63 [00:03<01:45, 1.73s/it] 1.23s/i
+ 24/29 16G 0.01257 0.008039 0 77 1280: 5%|â– | 3/63 [00:04<01:08, 1.15s/it] 1.23s/i
+ 24/29 16G 0.0126 0.008216 0 83 1280: 6%|â–‹ | 4/63 [00:06<01:46, 1.80s/it] 1.23s/i
+ 24/29 16G 0.01305 0.008405 0 92 1280: 10%|â–‰ | 6/63 [00:09<01:15, 1.33s/it] 1.23s/i
+ 24/29 16G 0.01377 0.008407 0 62 1280: 16%|█▌ | 10/63 [00:11<00:34, 1.52it/s]1.23s/i
+ 24/29 16G 0.01314 0.008108 0 49 1280: 21%|██ | 13/63 [00:13<00:27, 1.81it/s]1.23s/i
+ 24/29 16G 0.01312 0.008068 0 71 1280: 22%|██■| 14/63 [00:17<01:24, 1.72s/it]1.23s/i
+ 24/29 16G 0.01309 0.007991 0 63 1280: 24%|██■| 15/63 [00:28<03:39, 4.57s/it]1.23s/i
+ 24/29 16G 0.01304 0.007857 0 53 1280: 25%|██▌ | 16/63 [00:30<02:57, 3.77s/it]1.23s/i
+ 24/29 16G 0.0128 0.007724 0 64 1280: 32%|███■| 20/63 [00:32<00:51, 1.19s/it]1.23s/i
+ 24/29 16G 0.01279 0.007698 0 65 1280: 33%|███▎ | 21/63 [00:33<00:51, 1.24s/it]1.23s/i
+ 24/29 16G 0.01274 0.007479 0 65 1280: 38%|███▊ | 24/63 [00:36<00:37, 1.03it/s]1.23s/i
+ 24/29 16G 0.01273 0.007346 0 46 1280: 44%|████■| 28/63 [00:38<00:20, 1.70it/s]1.23s/i
+ 24/29 16G 0.01275 0.007427 0 68 1280: 48%|████▊ | 30/63 [00:42<00:41, 1.25s/it]1.23s/i
+ 24/29 16G 0.01283 0.007438 0 73 1280: 51%|█████ | 32/63 [00:43<00:28, 1.10it/s]1.23s/i
+ 24/29 16G 0.01283 0.007409 0 57 1280: 52%|█████■| 33/63 [00:45<00:37, 1.26s/it]1.23s/i
+ 24/29 16G 0.01285 0.007442 0 58 1280: 56%|█████▌ | 35/63 [00:48<00:32, 1.17s/it]1.23s/i
+ 24/29 16G 0.01287 0.007492 0 82 1280: 57%|█████▋ | 36/63 [00:50<00:40, 1.48s/it]1.23s/i
+ 24/29 16G 0.01291 0.007472 0 64 1280: 60%|██████ | 38/63 [00:52<00:31, 1.25s/it]1.23s/i
+ 24/29 16G 0.01289 0.007407 0 55 1280: 63%|██████▎ | 40/63 [00:54<00:21, 1.05it/s]1.23s/i
+ 24/29 16G 0.01287 0.007407 0 80 1280: 65%|██████▌ | 41/63 [00:57<00:34, 1.55s/it]1.23s/i
+ 24/29 16G 0.01297 0.00755 0 87 1280: 71%|███████■| 45/63 [00:59<00:13, 1.29it/s]1.23s/i
+ 24/29 16G 0.01299 0.007605 0 70 1280: 75%|███████■| 47/63 [01:01<00:15, 1.03it/s]1.23s/i
+ 24/29 16G 0.01296 0.007624 0 83 1280: 78%|███████▊ | 49/63 [01:02<00:11, 1.24it/s]1.23s/i
+ 24/29 16G 0.01301 0.007586 0 71 1280: 81%|████████ | 51/63 [01:05<00:12, 1.00s/it]1.23s/i
+ 24/29 16G 0.01303 0.007569 0 60 1280: 83%|████████▎ | 52/63 [01:09<00:21, 1.99s/it]1.23s/i
+ 24/29 16G 0.01305 0.007559 0 58 1280: 84%|████████■| 53/63 [01:15<00:32, 3.27s/it]1.23s/i
+ 24/29 16G 0.01301 0.007512 0 51 1280: 89%|████████▉ | 56/63 [01:20<00:13, 1.94s/it]1.23s/i
+ 24/29 16G 0.01301 0.007541 0 63 1280: 92%|█████████â–| 58/63 [01:21<00:05, 1.14s/it]1.23s/i
+ 24/29 16G 0.01302 0.00756 0 79 1280: 94%|█████████▎| 59/63 [01:23<00:06, 1.61s/it]1.23s/i
+ 24/29 16G 0.01303 0.007512 0 40 1280: 95%|█████████▌| 60/63 [01:24<00:04, 1.48s/it]1.23s/i
+ 24/29 16G 0.01299 0.007465 0 37 1280: 100%|██████████| 63/63 [01:30<00:00, 1.44s/it]1.23s/i
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:02<00:01, 2.24it/
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:03<00:00, 1.60it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.74it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.74it/
+ 25/29 16G 0.01182 0.007085 0 86 1280: 11%|â–ˆ | 7/63 [00:04<00:22, 2.44it/s] 1.74it/
+ 25/29 16G 0.01191 0.007044 0 65 1280: 13%|█▎ | 8/63 [00:04<00:19, 2.85it/s] 1.74it/
+ 25/29 16G 0.01229 0.007172 0 54 1280: 14%|█■| 9/63 [00:08<01:15, 1.39s/it] 1.74it/
+ 25/29 16G 0.0122 0.007308 0 55 1280: 21%|██ | 13/63 [00:10<00:34, 1.47it/s]1.74it/
+ 25/29 16G 0.01213 0.007361 0 80 1280: 22%|██■| 14/63 [00:11<00:38, 1.26it/s]1.74it/
+ 25/29 16G 0.01211 0.007371 0 47 1280: 25%|██▌ | 16/63 [00:14<00:54, 1.16s/it]1.74it/
+ 25/29 16G 0.01207 0.00739 0 70 1280: 27%|██▋ | 17/63 [00:17<01:21, 1.78s/it]1.74it/
+ 25/29 16G 0.01213 0.007328 0 60 1280: 29%|██▊ | 18/63 [00:20<01:38, 2.18s/it]1.74it/
+ 25/29 16G 0.01214 0.007425 0 82 1280: 38%|███▊ | 24/63 [00:22<00:21, 1.83it/s]1.74it/
+ 25/29 16G 0.01209 0.007357 0 54 1280: 40%|███▉ | 25/63 [00:23<00:19, 1.96it/s]1.74it/
+ 25/29 16G 0.01206 0.007359 0 73 1280: 43%|████▎ | 27/63 [00:26<00:40, 1.13s/it]1.74it/
+ 25/29 16G 0.01218 0.00722 0 61 1280: 48%|████▊ | 30/63 [00:27<00:18, 1.79it/s]1.74it/
+ 25/29 16G 0.0121 0.007196 0 62 1280: 51%|█████ | 32/63 [00:32<00:44, 1.43s/it]1.74it/
+ 25/29 16G 0.01206 0.00722 0 78 1280: 52%|█████■| 33/63 [00:35<00:55, 1.86s/it]1.74it/
+ 25/29 16G 0.012 0.007169 0 65 1280: 54%|█████■| 34/63 [00:38<00:59, 2.05s/it]1.74it/
+ 25/29 16G 0.012 0.007253 0 95 1280: 56%|█████▌ | 35/63 [00:41<01:05, 2.34s/it]1.74it/
+ 25/29 16G 0.0121 0.007344 0 89 1280: 62%|██████■| 39/63 [00:43<00:22, 1.05it/s]1.74it/
+ 25/29 16G 0.01208 0.007327 0 72 1280: 65%|██████▌ | 41/63 [00:45<00:21, 1.03it/s]1.74it/
+ 25/29 16G 0.01204 0.007289 0 52 1280: 67%|██████▋ | 42/63 [00:45<00:17, 1.23it/s]1.74it/
+ 25/29 16G 0.01202 0.007306 0 83 1280: 76%|███████▌ | 48/63 [00:48<00:05, 2.65it/s]1.74it/
+ 25/29 16G 0.01202 0.007335 0 73 1280: 79%|███████▉ | 50/63 [00:50<00:06, 2.09it/s]1.74it/
+ 25/29 16G 0.012 0.007283 0 58 1280: 86%|████████▌ | 54/63 [00:53<00:05, 1.52it/s]1.74it/
+ 25/29 16G 0.01197 0.00726 0 52 1280: 90%|█████████ | 57/63 [00:55<00:03, 1.54it/s]1.74it/
+ 25/29 16G 0.01202 0.007254 0 50 1280: 94%|█████████▎| 59/63 [00:56<00:02, 1.35it/s]1.74it/
+ 25/29 16G 0.012 0.007268 0 38 1280: 100%|██████████| 63/63 [00:58<00:00, 1.07it/s]1.74it/
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:02<00:07, 1.29s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.76it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.76it/
+ 26/29 16G 0.01358 0.007597 0 79 1280: 3%|â–Ž | 2/63 [00:02<01:14, 1.23s/it] 1.76it/
+ 26/29 16G 0.01265 0.007603 0 64 1280: 5%|â– | 3/63 [00:03<00:59, 1.01it/s] 1.76it/
+ 26/29 16G 0.01234 0.007508 0 66 1280: 6%|â–‹ | 4/63 [00:07<02:07, 2.16s/it] 1.76it/
+ 26/29 16G 0.01161 0.007305 0 68 1280: 10%|â–‰ | 6/63 [00:10<01:40, 1.76s/it] 1.76it/
+ 26/29 16G 0.01167 0.007571 0 44 1280: 14%|█■| 9/63 [00:13<00:54, 1.01s/it] 1.76it/
+ 26/29 16G 0.01184 0.007329 0 49 1280: 24%|██■| 15/63 [00:15<00:20, 2.30it/s]1.76it/
+ 26/29 16G 0.01173 0.007321 0 69 1280: 30%|███ | 19/63 [00:17<00:19, 2.20it/s]1.76it/
+ 26/29 16G 0.01177 0.00723 0 56 1280: 35%|███■| 22/63 [00:19<00:24, 1.67it/s]1.76it/
+ 26/29 16G 0.01164 0.007066 0 51 1280: 43%|████▎ | 27/63 [00:21<00:16, 2.19it/s]1.76it/
+ 26/29 16G 0.01164 0.007038 0 65 1280: 44%|████■| 28/63 [00:22<00:24, 1.41it/s]1.76it/
+ 26/29 16G 0.01163 0.007133 0 92 1280: 49%|████▉ | 31/63 [00:24<00:21, 1.48it/s]1.76it/
+ 26/29 16G 0.01157 0.007115 0 66 1280: 52%|█████■| 33/63 [00:26<00:22, 1.36it/s]1.76it/
+ 26/29 16G 0.01158 0.007135 0 82 1280: 54%|█████■| 34/63 [00:32<01:08, 2.37s/it]1.76it/
+ 26/29 16G 0.01159 0.007153 0 77 1280: 59%|█████▊ | 37/63 [00:34<00:31, 1.22s/it]1.76it/
+ 26/29 16G 0.01159 0.007103 0 66 1280: 67%|██████▋ | 42/63 [00:35<00:08, 2.48it/s]1.76it/
+ 26/29 16G 0.01158 0.007102 0 62 1280: 76%|███████▌ | 48/63 [00:40<00:05, 2.58it/s]1.76it/
+ 26/29 16G 0.01155 0.007081 0 50 1280: 78%|███████▊ | 49/63 [00:43<00:17, 1.22s/it]1.76it/
+ 26/29 16G 0.01151 0.007041 0 66 1280: 84%|████████■| 53/63 [00:50<00:12, 1.24s/it]1.76it/
+ 26/29 16G 0.01151 0.007008 0 54 1280: 92%|█████████â–| 58/63 [00:51<00:01, 2.54it/s]1.76it/
+ 26/29 16G 0.01151 0.006999 0 59 1280: 94%|█████████▎| 59/63 [00:55<00:05, 1.31s/it]1.76it/
+ 26/29 16G 0.01154 0.007051 0 40 1280: 100%|██████████| 63/63 [00:56<00:00, 1.12it/s]1.76it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.88it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.88it/
+ 27/29 16G 0.01056 0.007622 0 61 1280: 6%|â–‹ | 4/63 [00:05<01:28, 1.50s/it] 3.88it/
+ 27/29 16G 0.0104 0.007624 0 76 1280: 10%|â–‰ | 6/63 [00:07<01:05, 1.15s/it] 3.88it/
+ 27/29 16G 0.01069 0.006795 0 48 1280: 17%|█▋ | 11/63 [00:09<00:19, 2.62it/s]3.88it/
+ 27/29 16G 0.01082 0.006881 0 71 1280: 19%|█▉ | 12/63 [00:10<00:38, 1.32it/s]3.88it/
+ 27/29 16G 0.01084 0.006943 0 61 1280: 27%|██▋ | 17/63 [00:12<00:16, 2.84it/s]3.88it/
+ 27/29 16G 0.01086 0.006985 0 65 1280: 29%|██▊ | 18/63 [00:15<00:49, 1.10s/it]3.88it/
+ 27/29 16G 0.01074 0.006909 0 65 1280: 32%|███■| 20/63 [00:20<01:15, 1.76s/it]3.88it/
+ 27/29 16G 0.01076 0.006892 0 76 1280: 37%|███▋ | 23/63 [00:23<00:45, 1.14s/it]3.88it/
+ 27/29 16G 0.01088 0.006862 0 82 1280: 43%|████▎ | 27/63 [00:24<00:14, 2.42it/s]3.88it/
+ 27/29 16G 0.01093 0.00693 0 74 1280: 49%|████▉ | 31/63 [00:27<00:17, 1.87it/s]3.88it/
+ 27/29 16G 0.0109 0.006991 0 91 1280: 52%|█████■| 33/63 [00:28<00:10, 2.77it/s]3.88it/
+ 27/29 16G 0.01088 0.006945 0 58 1280: 54%|█████■| 34/63 [00:30<00:30, 1.05s/it]3.88it/
+ 27/29 16G 0.01086 0.006925 0 68 1280: 56%|█████▌ | 35/63 [00:35<00:57, 2.05s/it]3.88it/
+ 27/29 16G 0.01094 0.006847 0 48 1280: 59%|█████▊ | 37/63 [00:37<00:42, 1.65s/it]3.88it/
+ 27/29 16G 0.01093 0.006656 0 58 1280: 68%|██████▊ | 43/63 [00:39<00:08, 2.44it/s]3.88it/
+ 27/29 16G 0.01091 0.006683 0 77 1280: 70%|██████▉ | 44/63 [00:41<00:15, 1.26it/s]3.88it/
+ 27/29 16G 0.01089 0.006664 0 53 1280: 78%|███████▊ | 49/63 [00:43<00:05, 2.70it/s]3.88it/
+ 27/29 16G 0.01087 0.006633 0 45 1280: 79%|███████▉ | 50/63 [00:45<00:13, 1.02s/it]3.88it/
+ 27/29 16G 0.01084 0.006641 0 55 1280: 83%|████████▎ | 52/63 [00:51<00:18, 1.71s/it]3.88it/
+ 27/29 16G 0.01088 0.00676 0 92 1280: 86%|████████▌ | 54/63 [00:53<00:11, 1.33s/it]3.88it/
+ 27/29 16G 0.01088 0.006763 0 84 1280: 94%|█████████▎| 59/63 [00:54<00:01, 2.46it/s]3.88it/
+ 27/29 16G 0.01087 0.006764 0 64 1280: 97%|█████████▋| 61/63 [00:57<00:01, 1.01it/s]3.88it/
+ 27/29 16G 0.01083 0.006734 0 32 1280: 100%|██████████| 63/63 [00:57<00:00, 1.09it/s]3.88it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.09it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.09it/
+ 28/29 16G 0.01018 0.007349 0 76 1280: 10%|â–‰ | 6/63 [00:07<01:24, 1.48s/it] 4.09it/
+ 28/29 16G 0.01016 0.007167 0 88 1280: 19%|█▉ | 12/63 [00:09<00:19, 2.67it/s]4.09it/
+ 28/29 16G 0.0102 0.007207 0 73 1280: 21%|██ | 13/63 [00:10<00:37, 1.34it/s]4.09it/
+ 28/29 16G 0.01014 0.007172 0 66 1280: 29%|██▊ | 18/63 [00:13<00:17, 2.60it/s]4.09it/
+ 28/29 16G 0.01016 0.007019 0 43 1280: 30%|███ | 19/63 [00:15<00:42, 1.03it/s]4.09it/
+ 28/29 16G 0.01014 0.007022 0 70 1280: 33%|███▎ | 21/63 [00:20<01:10, 1.67s/it]4.09it/
+ 28/29 16G 0.01014 0.006946 0 73 1280: 37%|███▋ | 23/63 [00:22<00:51, 1.28s/it]4.09it/
+ 28/29 16G 0.01023 0.006915 0 66 1280: 44%|████■| 28/63 [00:24<00:15, 2.19it/s]4.09it/
+ 28/29 16G 0.01021 0.006933 0 64 1280: 46%|████▌ | 29/63 [00:26<00:26, 1.26it/s]4.09it/
+ 28/29 16G 0.01019 0.006882 0 70 1280: 54%|█████■| 34/63 [00:28<00:11, 2.56it/s]4.09it/
+ 28/29 16G 0.01021 0.006924 0 65 1280: 56%|█████▌ | 35/63 [00:30<00:27, 1.03it/s]4.09it/
+ 28/29 16G 0.01015 0.006875 0 49 1280: 63%|██████▎ | 40/63 [00:39<00:22, 1.04it/s]4.09it/
+ 28/29 16G 0.01009 0.006835 0 53 1280: 67%|██████▋ | 42/63 [00:40<00:18, 1.15it/s]4.09it/
+ 28/29 16G 0.01009 0.006835 0 53 1280: 67%|██████▋ | 42/63 [00:40<00:18, 1.15it/s]4.09it/
+
+ 28/29 16G 0.01013 0.00679 0 49 1280: 70%|██████▉ | 44/63 [01:02<01:56, 6.11s/it]4.09it/
+ 28/29 16G 0.01013 0.006735 0 43 1280: 71%|███████■| 45/63 [01:22<03:06, 10.37s/it]4.09it/
+ 28/29 16G 0.01014 0.006743 0 66 1280: 73%|███████▎ | 46/63 [01:35<03:06, 11.00s/it]4.09it/
+ 28/29 16G 0.01013 0.006711 0 51 1280: 75%|███████■| 47/63 [01:49<03:13, 12.07s/it]4.09it/
+ 28/29 16G 0.01013 0.00669 0 62 1280: 76%|███████▌ | 48/63 [01:57<02:40, 10.72s/it]4.09it/
+ 28/29 16G 0.01011 0.006669 0 67 1280: 78%|███████▊ | 49/63 [02:03<02:12, 9.43s/it]4.09it/
+ 28/29 16G 0.01011 0.006669 0 75 1280: 79%|███████▉ | 50/63 [02:09<01:48, 8.37s/it]4.09it/
+ 28/29 16G 0.0101 0.006667 0 65 1280: 81%|████████ | 51/63 [02:15<01:32, 7.73s/it]4.09it/
+ 28/29 16G 0.01007 0.006573 0 41 1280: 87%|████████▋ | 55/63 [02:18<00:18, 2.32s/it]4.09it/
+ 28/29 16G 0.0101 0.006574 0 69 1280: 97%|█████████▋| 61/63 [02:21<00:01, 1.79it/s]4.09it/
+ 28/29 16G 0.01005 0.006564 0 44 1280: 100%|██████████| 63/63 [02:22<00:00, 2.26s/it]4.09it/
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:02<00:06, 1.10s/i
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:07<00:14, 2.96s/i
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:09<00:10, 2.60s/i
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:12<00:01, 1.25s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:13<00:00, 1.63s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:13<00:00, 1.63s/i
+ 29/29 16G 0.009658 0.006052 0 67 1280: 2%|â– | 1/63 [00:07<07:30, 7.26s/it] 1.63s/i
+ 29/29 16G 0.009666 0.00671 0 90 1280: 11%|â–ˆ | 7/63 [00:11<00:38, 1.46it/s] 1.63s/i
+ 29/29 16G 0.00984 0.006937 0 84 1280: 19%|█▉ | 12/63 [00:14<00:25, 2.01it/s]1.63s/i
+ 29/29 16G 0.009816 0.006841 0 54 1280: 24%|██■| 15/63 [00:17<00:38, 1.25it/s]1.63s/i
+ 29/29 16G 0.009851 0.006885 0 81 1280: 25%|██▌ | 16/63 [00:17<00:33, 1.42it/s]1.63s/i
+ 29/29 16G 0.009791 0.006801 0 50 1280: 27%|██▋ | 17/63 [00:22<01:29, 1.94s/it]1.63s/i
+ 29/29 16G 0.009854 0.006723 0 45 1280: 29%|██▊ | 18/63 [00:30<02:42, 3.61s/it]1.63s/i
+ 29/29 16G 0.009643 0.006455 0 46 1280: 32%|███■| 20/63 [00:32<01:36, 2.25s/it]1.63s/i
+ 29/29 16G 0.009576 0.006565 0 96 1280: 35%|███■| 22/63 [00:34<01:06, 1.61s/it]1.63s/i
+ 29/29 16G 0.009573 0.006587 0 62 1280: 41%|████■| 26/63 [00:36<00:28, 1.31it/s]1.63s/i
+ 29/29 16G 0.009504 0.006589 0 56 1280: 48%|████▊ | 30/63 [00:38<00:19, 1.67it/s]1.63s/i
+ 29/29 16G 0.009524 0.00664 0 75 1280: 49%|████▉ | 31/63 [00:40<00:35, 1.10s/it]1.63s/i
+ 29/29 16G 0.009456 0.006639 0 68 1280: 54%|█████■| 34/63 [00:41<00:19, 1.51it/s]1.63s/i
+ 29/29 16G 0.009431 0.006594 0 54 1280: 56%|█████▌ | 35/63 [00:43<00:27, 1.03it/s]1.63s/i
+ 29/29 16G 0.009468 0.006538 0 60 1280: 59%|█████▊ | 37/63 [00:45<00:29, 1.15s/it]1.63s/i
+ 29/29 16G 0.009464 0.006522 0 64 1280: 60%|██████ | 38/63 [00:46<00:26, 1.06s/it]1.63s/i
+ 29/29 16G 0.009412 0.006511 0 50 1280: 63%|██████▎ | 40/63 [00:50<00:32, 1.41s/it]1.63s/i
+ 29/29 16G 0.009394 0.006516 0 41 1280: 68%|██████▊ | 43/63 [00:51<00:17, 1.14it/s]1.63s/i
+ 29/29 16G 0.009431 0.006574 0 94 1280: 75%|███████■| 47/63 [00:54<00:11, 1.36it/s]1.63s/i
+ 29/29 16G 0.009457 0.006538 0 53 1280: 78%|███████▊ | 49/63 [00:56<00:11, 1.26it/s]1.63s/i
+ 29/29 16G 0.009464 0.006584 0 92 1280: 81%|████████ | 51/63 [00:58<00:10, 1.10it/s]1.63s/i
+ 29/29 16G 0.00944 0.00657 0 72 1280: 83%|████████▎ | 52/63 [00:59<00:10, 1.05it/s]1.63s/i
+ 29/29 16G 0.009451 0.006576 0 63 1280: 86%|████████▌ | 54/63 [01:02<00:11, 1.23s/it]1.63s/i
+ 29/29 16G 0.009457 0.006541 0 62 1280: 90%|█████████ | 57/63 [01:04<00:06, 1.00s/it]1.63s/i
+ 29/29 16G 0.009457 0.006557 0 63 1280: 95%|█████████▌| 60/63 [01:06<00:02, 1.43it/s]1.63s/i
+ 29/29 16G 0.009464 0.006529 0 62 1280: 98%|█████████▊| 62/63 [01:08<00:00, 1.32it/s]1.63s/i
+ 29/29 16G 0.009465 0.006551 0 43 1280: 100%|██████████| 63/63 [01:09<00:00, 1.11s/it]1.63s/i
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:02<00:02, 1.53it/
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:04<00:03, 1.17s/i
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:07<00:03, 1.62s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:08<00:00, 1.09s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:08<00:00, 1.09s/i
+Fusing layers... d from runs/train/exp14/weights/best.pt, 42.6MB R mAP50 mAP50-95: 100%|██████████| 8/8 [00:08<00:00, 1.09s/i
+Fusing layers... d from runs/train/exp14/weights/best.pt, 42.6MB R mAP50 mAP50-95: 100%|██████████| 8/8 [00:08<00:00, 1.09s/i
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:03<00:10, 1.81s/i
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:05<00:10, 2.03s/i
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:08<00:08, 2.17s/i
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:10<00:01, 1.08s/i
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:11<00:00, 1.38s/i
+Results saved to [1mruns/train/exp14[22m Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:11<00:00, 1.38s/i
+Results saved to [1mruns/train/exp14[22m Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:11<00:00, 1.38s/i
+Results saved to [1mruns/train/exp14[22m Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:11<00:00, 1.38s/i
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_192253-294etqct/files/requirements.txt b/yolov5_model/wandb/run-20230226_192253-294etqct/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_192253-294etqct/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
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\ No newline at end of file
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--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/output.log
@@ -0,0 +1,1412 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Transferred 475/481 items from yolov5m.pt
+[34m[1mAMP: [39m[22mchecks passed ✅
+[34m[1moptimizer:[39m[22m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
+WARNING âš ï¸ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
+See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
+[34m[1mtrain: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels.cache... 1001 images, 163 backgrounds, 0 corrupt: 100
+[34m[1mval: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels.cache... 249 images, 39 backgrounds, 0 corrupt: 100%|███
+[34m[1mAutoAnchor: [39m[22m5.94 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
+Plotting labels to runs/train/exp15/labels.jpg...
+Image sizes 1280 train, 1280 val
+Using 16 dataloader workers
+Logging results to [1mruns/train/exp15
+Starting training for 50 epochs...
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
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+
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+ all 249 604 0.364 0.696 0.43 0.128
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+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.84it/
+ all 249 604 0.261 0.808 0.265 0.0927
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
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+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.62it/
+ all 249 604 0.272 0.715 0.257 0.0703
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+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.85it/
+ all 249 604 0.766 0.936 0.842 0.378
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+
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+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:08<00:00, 1.10s/i
+ all 249 604 0.862 0.969 0.971 0.488
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+ 5/49 16G 0.04499 0.01469 0 25 1280: 100%|██████████| 63/63 [00:59<00:00, 1.07it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.55it/
+ all 249 604 0.964 0.986 0.99 0.498
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+ 6/49 16G 0.04063 0.01392 0 47 1280: 100%|██████████| 63/63 [00:59<00:00, 1.06it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.09it/
+ all 249 604 0.998 0.997 0.995 0.711
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+ 7/49 16G 0.03671 0.01373 0 33 1280: 100%|██████████| 63/63 [01:08<00:00, 1.09s/it]
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.79it/
+ all 249 604 0.998 0.997 0.995 0.573
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+
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+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:07<00:00, 1.06it/
+ all 249 604 1 0.997 0.995 0.601
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+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.05it/
+ all 249 604 1 0.998 0.995 0.733
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+ 10/49 16G 0.03023 0.01205 0 33 1280: 100%|██████████| 63/63 [00:52<00:00, 1.20it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.92it/
+ all 249 604 1 0.997 0.995 0.743
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+ 11/49 16G 0.02991 0.01051 0 49 1280: 8%|â–Š | 5/63 [00:03<00:29, 1.95it/s]
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.678██████| 63/63 [01:02<00:00, 1.00it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.678██████| 63/63 [01:02<00:00, 1.00it/s]
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+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s]
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:02<00:01, 2.19it/
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+ 13/49 16G 0.02621 0.01083 0 73 1280: 44%|████■| 28/63 [00:37<00:25, 1.38it/s]2.29it/
+ 13/49 16G 0.02624 0.01051 0 41 1280: 51%|█████ | 32/63 [00:39<00:14, 2.15it/s]2.29it/
+ 13/49 16G 0.02618 0.0106 0 84 1280: 54%|█████■| 34/63 [00:40<00:16, 1.79it/s]2.29it/
+ 13/49 16G 0.02619 0.01048 0 52 1280: 59%|█████▊ | 37/63 [00:55<01:02, 2.40s/it]2.29it/
+ 13/49 16G 0.02617 0.01048 0 64 1280: 60%|██████ | 38/63 [00:56<00:49, 1.97s/it]2.29it/
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+ 13/49 16G 0.02617 0.01048 0 64 1280: 60%|██████ | 38/63 [00:56<00:49, 1.97s/it]2.29it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.765: 0%| | 0/8 [00:00<?, ?it/s].29it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.765: 0%| | 0/8 [00:00<?, ?it/s].29it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.765: 0%| | 0/8 [00:00<?, ?it/s].29it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.792: 12%|█▎ | 1/8 [00:01<00:07, 1.05s/i
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.792: 12%|█▎ | 1/8 [00:01<00:07, 1.05s/i
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.792: 12%|█▎ | 1/8 [00:01<00:07, 1.05s/i
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.792: 12%|█▎ | 1/8 [00:01<00:07, 1.05s/i
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.792: 12%|█▎ | 1/8 [00:01<00:07, 1.05s/i
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.792: 12%|█▎ | 1/8 [00:01<00:07, 1.05s/i
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.792: 12%|█▎ | 1/8 [00:01<00:07, 1.05s/i
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.827: 38%|███▊ | 3/8 [00:00<00:01, 3.88it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.827: 38%|███▊ | 3/8 [00:00<00:01, 3.88it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.827: 38%|███▊ | 3/8 [00:00<00:01, 3.88it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 0%| | 0/8 [00:00<?, ?it/s].88it/
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+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:02, 2.22it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.786: 25%|██▌ | 2/8 [00:00<00:02, 2.22it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.834: 38%|███▊ | 3/8 [00:00<00:01, 3.76it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.834: 38%|███▊ | 3/8 [00:00<00:01, 3.76it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.834: 38%|███▊ | 3/8 [00:00<00:01, 3.76it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.834: 38%|███▊ | 3/8 [00:00<00:01, 3.76it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.858: 50%|█████ | 4/8 [00:01<00:01, 3.81it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.846: 38%|███▊ | 3/8 [00:00<00:01, 3.31it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:01<00:01, 2.60it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:01<00:01, 2.60it/
+ 0%| | 0/63 [00:00<?, ?it/s]tances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:01<00:01, 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ 26/49 16G 0.02108 0.007933 0 52 1280: 2%|â– | 1/63 [00:09<09:39, 9.35s/it] 2.60it/
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 12%|█▎ | 1/8 [00:00<00:06, 1.17it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.848: 38%|███▊ | 3/8 [00:00<00:01, 3.51it/
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 75%|███████▌ | 6/8 [00:01<00:00, 3.82it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ all 249 604 0.999 0.997 0.995 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.882: 38%|███▊ | 3/8 [00:01<00:01, 2.82it/
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.883: 62%|██████▎ | 5/8 [00:01<00:00, 3.61it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ all 249 604 0.998 0.998 0.995 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.865: 38%|███▊ | 3/8 [00:00<00:01, 3.54it/
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ all 249 604 0.999 0.997 0.995 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.9: 12%|█▎ | 1/8 [00:00<00:04, 1.50it/
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.891: 88%|████████▊ | 7/8 [00:01<00:00, 3.67it/
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.893: 62%|██████▎ | 5/8 [00:01<00:00, 3.67it/
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ all 249 604 1 0.998 0.995 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.897: 0%| | 0/8 [00:00<?, ?it/s].67it/
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+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.56it/
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+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 2.68it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.904: 12%|█▎ | 1/8 [00:00<00:02, 2.68it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.904: 12%|█▎ | 1/8 [00:00<00:02, 2.68it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.904: 12%|█▎ | 1/8 [00:00<00:02, 2.68it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.907: 38%|███▊ | 3/8 [00:00<00:01, 3.73it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.906: 0%| | 0/8 [00:00<?, ?it/s].73it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.916: 12%|█▎ | 1/8 [00:00<00:04, 1.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.916: 12%|█▎ | 1/8 [00:00<00:04, 1.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.916: 12%|█▎ | 1/8 [00:00<00:04, 1.67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.916: 12%|█▎ | 1/8 [00:00<00:04, 1.67it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.91: 12%|█▎ | 1/8 [00:00<00:04, 1.67it/
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].67it/
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+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.917: 0%| | 0/8 [00:00<?, ?it/s].67it/
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.919: 88%|████████▊ | 7/8 [00:01<00:00, 4.10it/
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.914: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 0%| | 0/8 [00:00<?, ?it/s].95it/
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ all 249 604 0.998 0.998 0.995 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.929: 50%|█████ | 4/8 [00:01<00:01, 2.27it/
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ all 249 604 0.998 0.998 0.995 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.924: 38%|███▊ | 3/8 [00:01<00:01, 2.66it/
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
+50 epochs completed in 1.089 hours. 604 0.998 0.998 0.995 0.927: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
+Model summary: 212 layers, 20852934 parameters, 0 gradients, 47.9 GFLOPs0.995 0.927: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
+Model summary: 212 layers, 20852934 parameters, 0 gradients, 47.9 GFLOPs0.995 0.927: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
+ all 249 604 0.998 0.998 0.995 0.935: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
+ all 249 604 0.998 0.998 0.995 0.935: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
+ all 249 604 0.998 0.998 0.995 0.935: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
+ all 249 604 0.998 0.998 0.995 0.935: 50%|█████ | 4/8 [00:01<00:01, 3.71it/
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/requirements.txt b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/requirements.txt
@@ -0,0 +1,216 @@
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\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/wandb-metadata.json b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/wandb-metadata.json
new file mode 100644
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+ "commit": "4d70f4429752b1cbed4a5363ecf8c004a7a149a8"
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+ "host": "hal-dgx",
+ "username": "akhot2",
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diff --git a/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/wandb-summary.json b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/files/wandb-summary.json
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\ No newline at end of file
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new file mode 100644
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diff --git a/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/logs/debug.log b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..774247ec610852eaadcedb0666d25ce490e5b0f1
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diff --git a/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/run-i0ykd1a6.wandb b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/run-i0ykd1a6.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..ad0f4cfafaec5431bea2bde0f39db398aa7d2b8a
Binary files /dev/null and b/yolov5_model/wandb/run-20230226_201241-i0ykd1a6/run-i0ykd1a6.wandb differ
diff --git a/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/conda-environment.yaml b/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/conda-environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cdca88046552a769a1422af9039909dbf6cce9e
--- /dev/null
+++ b/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/conda-environment.yaml
@@ -0,0 +1,332 @@
+name: akhot2
+channels:
+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
+dependencies:
+ - _libgcc_mutex=0.1=main
+ - _openmp_mutex=5.1=1_gnu
+ - _py-xgboost-mutex=2.0=cpu_0
+ - _tflow_select=2.3.0=mkl
+ - aiohttp=3.8.1=py39h7f8727e_1
+ - aiosignal=1.2.0=pyhd3eb1b0_0
+ - argon2-cffi=21.3.0=pyhd3eb1b0_0
+ - argon2-cffi-bindings=21.2.0=py39h7f8727e_0
+ - astor=0.8.1=py39h06a4308_0
+ - asttokens=2.0.5=pyhd3eb1b0_0
+ - astunparse=1.6.3=py_0
+ - async-timeout=4.0.1=pyhd3eb1b0_0
+ - atk-1.0=2.36.0=ha1a6a79_0
+ - attrs=21.4.0=pyhd3eb1b0_0
+ - awkward=0.15.0=pyhd3deb0d_0
+ - backcall=0.2.0=pyhd3eb1b0_0
+ - beautifulsoup4=4.11.1=py39h06a4308_0
+ - blas=1.0=mkl
+ - bleach=4.1.0=pyhd3eb1b0_0
+ - blinker=1.4=py39h06a4308_0
+ - blosc=1.21.0=h4ff587b_1
+ - bottleneck=1.3.4=py39hce1f21e_0
+ - brotli=1.0.9=he6710b0_2
+ - brotlipy=0.7.0=py39h27cfd23_1003
+ - bzip2=1.0.8=h7b6447c_0
+ - c-ares=1.18.1=h7f8727e_0
+ - ca-certificates=2023.01.10=h06a4308_0
+ - cachetools=4.2.2=pyhd3eb1b0_0
+ - cairo=1.16.0=h19f5f5c_2
+ - certifi=2022.12.7=pyhd8ed1ab_0
+ - cffi=1.15.0=py39hd667e15_1
+ - charset-normalizer=2.0.4=pyhd3eb1b0_0
+ - click=8.0.4=py39h06a4308_0
+ - cloudpickle=2.0.0=pyhd3eb1b0_0
+ - colorama=0.4.4=pyhd3eb1b0_0
+ - cramjam=2.5.0=py39h860a657_0
+ - cryptography=3.4.8=py39hd23ed53_0
+ - cudatoolkit=11.3.1=h2bc3f7f_2
+ - cython=0.29.28=py39h295c915_0
+ - dataclasses=0.8=pyh6d0b6a4_7
+ - dbus=1.13.18=hb2f20db_0
+ - debugpy=1.5.1=py39h295c915_0
+ - decorator=5.1.1=pyhd3eb1b0_0
+ - defusedxml=0.7.1=pyhd3eb1b0_0
+ - detectron2=0.5=py39hf2442f4_1
+ - docker-pycreds=0.4.0=pyhd3eb1b0_0
+ - entrypoints=0.4=py39h06a4308_0
+ - et_xmlfile=1.1.0=py39h06a4308_0
+ - executing=0.8.3=pyhd3eb1b0_0
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+ - fastparquet=0.8.1=py39hd257fcd_0
+ - ffmpeg=4.2.2=h20bf706_0
+ - filelock=3.7.1=pyhd8ed1ab_0
+ - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
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+ - font-ttf-source-code-pro=2.030=hd3eb1b0_0
+ - font-ttf-ubuntu=0.83=h8b1ccd4_0
+ - fontconfig=2.13.1=h6c09931_0
+ - fonts-anaconda=1=h8fa9717_0
+ - fonts-conda-ecosystem=1=hd3eb1b0_0
+ - fonttools=4.25.0=pyhd3eb1b0_0
+ - freetype=2.11.0=h70c0345_0
+ - fribidi=1.0.10=h7b6447c_0
+ - frozenlist=1.2.0=py39h7f8727e_0
+ - fsspec=2022.5.0=pyhd8ed1ab_0
+ - future=0.18.2=py39h06a4308_1
+ - fvcore=0.1.5.post20220506=pyhd8ed1ab_0
+ - gdk-pixbuf=2.42.8=h433bba3_0
+ - gdown=4.5.1=pyhd8ed1ab_0
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+ - gitdb=4.0.7=pyhd3eb1b0_0
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+ - glib=2.69.1=h4ff587b_1
+ - gmp=6.2.1=h295c915_3
+ - gnutls=3.6.15=he1e5248_0
+ - gobject-introspection=1.68.0=py39he41a700_3
+ - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+ - google-pasta=0.2.0=pyhd3eb1b0_0
+ - graphite2=1.3.14=h295c915_1
+ - graphviz=2.50.0=h3cd0ef9_0
+ - gst-plugins-base=1.14.0=h8213a91_2
+ - gstreamer=1.14.0=h28cd5cc_2
+ - gtk2=2.24.33=h73c1081_2
+ - gts=0.7.6=hb67d8dd_3
+ - h5py=3.6.0=py39ha0f2276_0
+ - harfbuzz=4.3.0=hd55b92a_0
+ - hdf5=1.10.6=hb1b8bf9_0
+ - icu=58.2=he6710b0_3
+ - importlib-metadata=4.11.3=py39h06a4308_0
+ - intel-openmp=2021.4.0=h06a4308_3561
+ - ipykernel=6.9.1=py39h06a4308_0
+ - ipython=8.3.0=py39h06a4308_0
+ - ipython_genutils=0.2.0=pyhd3eb1b0_1
+ - ipywidgets=7.6.5=pyhd3eb1b0_1
+ - jedi=0.18.1=py39h06a4308_1
+ - jinja2=3.0.3=pyhd3eb1b0_0
+ - joblib=1.1.0=pyhd3eb1b0_0
+ - jpeg=9e=h7f8727e_0
+ - jsonschema=4.4.0=py39h06a4308_0
+ - jupyter=1.0.0=py39h06a4308_7
+ - jupyter_client=7.2.2=py39h06a4308_0
+ - jupyter_console=6.4.3=pyhd3eb1b0_0
+ - jupyter_core=4.10.0=py39h06a4308_0
+ - jupyterlab_pygments=0.1.2=py_0
+ - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+ - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+ - kiwisolver=1.4.2=py39h295c915_0
+ - lame=3.100=h7b6447c_0
+ - lcms2=2.12=h3be6417_0
+ - ld_impl_linux-64=2.38=h1181459_1
+ - libffi=3.3=he6710b0_2
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgd=2.3.3=h695aa2c_1
+ - libgfortran-ng=7.5.0=ha8ba4b0_17
+ - libgfortran4=7.5.0=ha8ba4b0_17
+ - libgomp=11.2.0=h1234567_1
+ - libidn2=2.3.2=h7f8727e_0
+ - libllvm11=11.1.0=h3826bc1_1
+ - libopus=1.3.1=h7b6447c_0
+ - libpng=1.6.37=hbc83047_0
+ - libprotobuf=3.20.3=he621ea3_0
+ - librsvg=2.54.4=h19fe530_0
+ - libsodium=1.0.18=h7b6447c_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtasn1=4.16.0=h27cfd23_0
+ - libtiff=4.2.0=h2818925_1
+ - libtool=2.4.6=h295c915_1008
+ - libunistring=0.9.10=h27cfd23_0
+ - libuuid=1.0.3=h7f8727e_2
+ - libuv=1.40.0=h7b6447c_0
+ - libvpx=1.7.0=h439df22_0
+ - libwebp=1.2.2=h55f646e_0
+ - libwebp-base=1.2.2=h7f8727e_0
+ - libxcb=1.15=h7f8727e_0
+ - libxgboost=1.5.1=cpu_h3d145d1_2
+ - libxml2=2.9.14=h74e7548_0
+ - lime=0.2.0.1=pyh9f0ad1d_0
+ - lz4-c=1.9.3=h295c915_1
+ - lzo=2.10=h7b6447c_2
+ - markdown=3.3.4=py39h06a4308_0
+ - markupsafe=2.1.1=py39h7f8727e_0
+ - matplotlib=3.5.1=py39h06a4308_1
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+ - matplotlib-inline=0.1.2=pyhd3eb1b0_2
+ - mistune=0.8.4=py39h27cfd23_1000
+ - mkl=2021.4.0=h06a4308_640
+ - mkl-service=2.4.0=py39h7f8727e_0
+ - mkl_fft=1.3.1=py39hd3c417c_0
+ - mkl_random=1.2.2=py39h51133e4_0
+ - mock=4.0.3=pyhd3eb1b0_0
+ - multidict=5.2.0=py39h7f8727e_2
+ - munkres=1.1.4=py_0
+ - nbclient=0.5.13=py39h06a4308_0
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+ - nest-asyncio=1.5.5=py39h06a4308_0
+ - nettle=3.7.3=hbbd107a_1
+ - ninja=1.10.2=h06a4308_5
+ - ninja-base=1.10.2=hd09550d_5
+ - notebook=6.4.11=py39h06a4308_0
+ - numexpr=2.8.1=py39h6abb31d_0
+ - numpy-base=1.21.5=py39ha15fc14_3
+ - oauthlib=3.2.0=pyhd3eb1b0_0
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+ - packaging=21.3=pyhd3eb1b0_0
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+ - parso=0.8.3=pyhd3eb1b0_0
+ - pathtools=0.1.2=pyhd3eb1b0_1
+ - pcre=8.45=h295c915_0
+ - pexpect=4.8.0=pyhd3eb1b0_3
+ - pickleshare=0.7.5=pyhd3eb1b0_1003
+ - pillow=9.0.1=py39h22f2fdc_0
+ - pip=21.2.4=py39h06a4308_0
+ - pixman=0.40.0=h7f8727e_1
+ - portalocker=2.3.0=py39h06a4308_0
+ - prometheus_client=0.13.1=pyhd3eb1b0_0
+ - promise=2.3=py39h06a4308_0
+ - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+ - prompt_toolkit=3.0.20=hd3eb1b0_0
+ - psutil=5.8.0=py39h27cfd23_1
+ - ptyprocess=0.7.0=pyhd3eb1b0_2
+ - pure_eval=0.2.2=pyhd3eb1b0_0
+ - py-xgboost=1.5.1=cpu_py39h4655687_2
+ - pyasn1=0.4.8=pyhd3eb1b0_0
+ - pyasn1-modules=0.2.8=py_0
+ - pycocotools=2.0.2=py39hce5d2b2_2
+ - pycparser=2.21=pyhd3eb1b0_0
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+ - pysocks=1.7.1=py39h06a4308_0
+ - pytables=3.6.1=py39h77479fe_1
+ - pythia8=8.305=py39he80948d_0
+ - python=3.9.12=h12debd9_1
+ - python-dateutil=2.8.2=pyhd3eb1b0_0
+ - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
+ - python-graphviz=0.20.1=pyh22cad53_0
+ - python_abi=3.9=2_cp39
+ - pytorch=1.11.0=py3.9_cuda11.3_cudnn8.2.0_0
+ - pytorch-model-summary=0.1.1=py_0
+ - pytorch-mutex=1.0=cuda
+ - pytz=2022.1=py39h06a4308_0
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+ - requests=2.27.1=pyhd3eb1b0_0
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+ - send2trash=1.8.0=pyhd3eb1b0_1
+ - sentry-sdk=1.5.12=pyhd8ed1ab_0
+ - setproctitle=1.2.2=py39h27cfd23_1004
+ - shap=0.40.0=py39hde0f152_1
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+ - sip=4.19.13=py39h295c915_0
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+ - soupsieve=2.3.1=pyhd3eb1b0_0
+ - sqlite=3.38.3=hc218d9a_0
+ - stack_data=0.2.0=pyhd3eb1b0_0
+ - tabulate=0.8.9=py39h06a4308_0
+ - tbb=2021.5.0=hd09550d_0
+ - tensorboard-plugin-wit=1.6.0=py_0
+ - termcolor=1.1.0=py39h06a4308_1
+ - terminado=0.13.1=py39h06a4308_0
+ - testpath=0.5.0=pyhd3eb1b0_0
+ - threadpoolctl=2.2.0=pyh0d69192_0
+ - tk=8.6.12=h1ccaba5_0
+ - torchaudio=0.11.0=py39_cu113
+ - torchinfo=1.6.5=pyhd8ed1ab_0
+ - torchvision=0.12.0=py39_cu113
+ - tornado=6.1=py39h27cfd23_0
+ - tqdm=4.64.0=py39h06a4308_0
+ - traitlets=5.1.1=pyhd3eb1b0_0
+ - typing_extensions=4.1.1=pyh06a4308_0
+ - tzdata=2022a=hda174b7_0
+ - uproot-methods=0.9.2=pyhd8ed1ab_0
+ - urllib3=1.26.9=py39h06a4308_0
+ - wandb=0.12.15=pyhd8ed1ab_0
+ - wcwidth=0.2.5=pyhd3eb1b0_0
+ - webencodings=0.5.1=py39h06a4308_1
+ - werkzeug=2.0.3=pyhd3eb1b0_0
+ - widgetsnbextension=3.5.2=py39h06a4308_0
+ - x264=1!157.20191217=h7b6447c_0
+ - xgboost=1.5.1=cpu_py39h4655687_2
+ - xz=5.2.5=h7f8727e_1
+ - yacs=0.1.6=pyhd3eb1b0_1
+ - yaml=0.2.5=h7b6447c_0
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+ - zlib=1.2.13=h5eee18b_0
+ - zstd=1.5.2=ha4553b6_0
+ - pip:
+ - absl-py==1.1.0
+ - chardet==4.0.0
+ - commonmark==0.9.1
+ - cycler==0.10.0
+ - energyflow==1.3.2
+ - flatbuffers==1.12
+ - gast==0.3.3
+ - google-auth==1.35.0
+ - grpcio==1.32.0
+ - idna==2.10
+ - imageio==2.19.3
+ - iniconfig==1.1.1
+ - keras==2.9.0
+ - keras-applications==1.0.8
+ - lbn==1.2.2
+ - libclang==14.0.1
+ - llvmlite==0.36.0
+ - modelstore==0.0.74
+ - networkx==2.8.3
+ - numba==0.53.0
+ - numpy==1.20.0
+ - opencv-python==4.7.0.68
+ - pd4ml==0.3
+ - pillow-heif==0.9.3
+ - pluggy==1.0.0
+ - protobuf==3.19.4
+ - py==1.11.0
+ - pyparsing==2.4.7
+ - pytest==7.1.2
+ - python-dotenv==0.21.1
+ - pywavelets==1.3.0
+ - requests-toolbelt==0.10.1
+ - rich==12.4.4
+ - roboflow==0.2.29
+ - scikit-image==0.19.2
+ - setuptools==67.3.2
+ - six==1.15.0
+ - tensorboard==2.9.1
+ - tensorboard-data-server==0.6.1
+ - tensorflow==2.9.1
+ - tensorflow-decision-forests==0.2.6
+ - tensorflow-estimator==2.9.0
+ - tensorflow-io-gcs-filesystem==0.26.0
+ - thop==0.1.1-2209072238
+ - tifffile==2022.5.4
+ - tomli==2.0.1
+ - typing-extensions==3.7.4.3
+ - vector==0.8.5
+ - wasserstein==1.0.1
+ - wget==3.2
+ - wheel==0.38.4
+ - wrapt==1.12.1
+ - wurlitzer==3.0.2
+prefix: /raid/projects/akhot2/conda/envs/akhot2
diff --git a/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/config.yaml b/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9e2040e3b6ee997aa3d30687757179ee51d6fcdc
--- /dev/null
+++ b/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/config.yaml
@@ -0,0 +1,178 @@
+wandb_version: 1
+
+_wandb:
+ desc: null
+ value:
+ cli_version: 0.12.15
+ framework: torch
+ is_jupyter_run: false
+ is_kaggle_kernel: false
+ python_version: 3.9.12
+ start_time: 1678062265
+ t:
+ 1:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 2:
+ - 1
+ - 2
+ - 3
+ - 41
+ - 55
+ 3:
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+ - 13
+ - 16
+ 4: 3.9.12
+ 5: 0.12.15
+ 8:
+ - 5
+artifact_alias:
+ desc: null
+ value: latest
+batch_size:
+ desc: null
+ value: 16
+bbox_interval:
+ desc: null
+ value: -1
+bucket:
+ desc: null
+ value: ''
+cache:
+ desc: null
+ value: null
+cfg:
+ desc: null
+ value: ''
+cos_lr:
+ desc: null
+ value: false
+data:
+ desc: null
+ value: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
+device:
+ desc: null
+ value: ''
+entity:
+ desc: null
+ value: null
+epochs:
+ desc: null
+ value: 50
+evolve:
+ desc: null
+ value: null
+exist_ok:
+ desc: null
+ value: false
+freeze:
+ desc: null
+ value:
+ - 0
+hyp:
+ desc: null
+ value:
+ anchor_t: 4.0
+ box: 0.05
+ cls: 0.5
+ cls_pw: 1.0
+ copy_paste: 0.0
+ degrees: 0.0
+ fl_gamma: 0.0
+ fliplr: 0.5
+ flipud: 0.0
+ hsv_h: 0.015
+ hsv_s: 0.7
+ hsv_v: 0.4
+ iou_t: 0.2
+ lr0: 0.01
+ lrf: 0.01
+ mixup: 0.0
+ momentum: 0.937
+ mosaic: 1.0
+ obj: 1.0
+ obj_pw: 1.0
+ perspective: 0.0
+ scale: 0.5
+ shear: 0.0
+ translate: 0.1
+ warmup_bias_lr: 0.1
+ warmup_epochs: 3.0
+ warmup_momentum: 0.8
+ weight_decay: 0.0005
+image_weights:
+ desc: null
+ value: false
+imgsz:
+ desc: null
+ value: 1280
+label_smoothing:
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--- /dev/null
+++ b/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/output.log
@@ -0,0 +1,1004 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Transferred 475/481 items from yolov5m.pt
+[34m[1mAMP: [39m[22mchecks passed ✅
+[34m[1moptimizer:[39m[22m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
+WARNING âš ï¸ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
+See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
+[34m[1mtrain: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels... 1001 images, 145 backgrounds, 0 corrupt: 100%|████
+[34m[1mtrain: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels.cache
+[34m[1mval: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels... 249 images, 37 backgrounds, 0 corrupt: 100%|█████████
+[34m[1mval: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels.cache
+[34m[1mAutoAnchor: [39m[22m6.41 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
+Plotting labels to runs/train/6beetle_7-non_clean_bkg_overlap/labels.jpg...
+Image sizes 1280 train, 1280 val
+Using 8 dataloader workers
+Logging results to [1mruns/train/6beetle_7-non_clean_bkg_overlap
+Starting training for 50 epochs...
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+
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+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:05<00:00, 1.46it/
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+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ 11/49 16G 0.02959 0.01732 0 42 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:01<00:00, 3.95it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.99it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.99it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.99it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.99it/
+ 13/49 16G 0.02605 0.01722 0 102 1280: 21%|██ | 13/63 [00:26<00:46, 1.07it/s]3.99it/
+ 13/49 16G 0.026 0.01797 0 134 1280: 33%|███▎ | 21/63 [00:35<00:18, 2.23it/s]3.99it/
+ 13/49 16G 0.02608 0.01804 0 94 1280: 46%|████▌ | 29/63 [00:47<00:15, 2.18it/s]3.99it/
+ 13/49 16G 0.02605 0.01788 0 95 1280: 49%|████▉ | 31/63 [00:59<01:29, 2.81s/it]3.99it/
+ 13/49 16G 0.02654 0.01793 0 93 1280: 59%|█████▊ | 37/63 [01:00<00:12, 2.03it/s]3.99it/
+ 13/49 16G 0.02682 0.01801 0 81 1280: 68%|██████▊ | 43/63 [01:13<00:16, 1.22it/s]3.99it/
+ 13/49 16G 0.02677 0.01784 0 83 1280: 71%|███████■| 45/63 [01:13<00:08, 2.03it/s]3.99it/
+ 13/49 16G 0.02687 0.01786 0 68 1280: 84%|████████■| 53/63 [01:27<00:04, 2.04it/s]3.99it/
+ 13/49 16G 0.02701 0.01798 0 80 1280: 90%|█████████ | 57/63 [01:39<00:08, 1.49s/it]3.99it/
+ 13/49 16G 0.02699 0.01796 0 119 1280: 97%|█████████▋| 61/63 [01:40<00:00, 2.01it/s]3.99it/
+ 13/49 16G 0.02705 0.01791 0 43 1280: 100%|██████████| 63/63 [01:52<00:00, 1.78s/it]3.99it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.12it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.12it/
+ 14/49 16G 0.02768 0.01721 0 98 1280: 14%|█■| 9/63 [00:17<02:24, 2.68s/it] 4.12it/
+ 14/49 16G 0.0279 0.01699 0 75 1280: 16%|█▌ | 10/63 [00:20<02:21, 2.67s/it]4.12it/
+ 14/49 16G 0.02763 0.01686 0 102 1280: 22%|██■| 14/63 [00:24<00:55, 1.14s/it]4.12it/
+ 14/49 16G 0.02759 0.01653 0 62 1280: 30%|███ | 19/63 [00:33<00:40, 1.07it/s]4.12it/
+ 14/49 16G 0.02708 0.01634 0 85 1280: 35%|███■| 22/63 [00:34<00:20, 2.00it/s]4.12it/
+ 14/49 16G 0.02692 0.01656 0 105 1280: 38%|███▊ | 24/63 [00:45<01:43, 2.65s/it]4.12it/
+ 14/49 16G 0.02703 0.01653 0 106 1280: 48%|████▊ | 30/63 [00:46<00:15, 2.11it/s]4.12it/
+ 14/49 16G 0.02717 0.01677 0 72 1280: 60%|██████ | 38/63 [00:59<00:12, 2.08it/s]4.12it/
+ 14/49 16G 0.02705 0.01687 0 84 1280: 73%|███████▎ | 46/63 [01:13<00:08, 1.97it/s]4.12it/
+ 14/49 16G 0.02703 0.01692 0 104 1280: 78%|███████▊ | 49/63 [01:25<00:27, 2.00s/it]4.12it/
+ 14/49 16G 0.027 0.01703 0 110 1280: 86%|████████▌ | 54/63 [01:26<00:04, 2.04it/s]4.12it/
+ 14/49 16G 0.0269 0.01701 0 90 1280: 98%|█████████▊| 62/63 [01:39<00:00, 2.04it/s]4.12it/
+ 14/49 16G 0.02686 0.01697 0 45 1280: 100%|██████████| 63/63 [01:45<00:00, 1.67s/it]4.12it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.05it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.05it/
+ 15/49 16G 0.02555 0.01665 0 119 1280: 11%|â–ˆ | 7/63 [00:02<00:31, 1.76it/s] 4.05it/
+ 15/49 16G 0.02559 0.01698 0 105 1280: 17%|█▋ | 11/63 [00:10<00:53, 1.04s/it]4.05it/
+ 15/49 16G 0.02547 0.01684 0 84 1280: 19%|█▉ | 12/63 [00:11<00:51, 1.00s/it]4.05it/
+ 15/49 16G 0.02528 0.01683 0 92 1280: 22%|██■| 14/63 [00:15<01:04, 1.32s/it]4.05it/
+ 15/49 16G 0.02528 0.0172 0 133 1280: 24%|██■| 15/63 [00:23<02:33, 3.20s/it]4.05it/
+ 15/49 16G 0.02531 0.0174 0 114 1280: 25%|██▌ | 16/63 [00:29<03:17, 4.19s/it]4.05it/
+ 15/49 16G 0.02535 0.0173 0 122 1280: 32%|███■| 20/63 [00:31<00:57, 1.33s/it]4.05it/
+ 15/49 16G 0.0254 0.01744 0 104 1280: 35%|███■| 22/63 [00:33<00:47, 1.15s/it]4.05it/
+ 15/49 16G 0.02532 0.0174 0 91 1280: 37%|███▋ | 23/63 [00:35<01:02, 1.55s/it]4.05it/
+ 15/49 16G 0.02557 0.01718 0 78 1280: 44%|████■| 28/63 [00:45<00:36, 1.03s/it]4.05it/
+ 15/49 16G 0.02546 0.01714 0 90 1280: 48%|████▊ | 30/63 [00:48<00:42, 1.29s/it]4.05it/
+ 15/49 16G 0.02543 0.01727 0 130 1280: 49%|████▉ | 31/63 [00:50<00:44, 1.40s/it]4.05it/
+ 15/49 16G 0.02543 0.01718 0 97 1280: 54%|█████■| 34/63 [00:57<00:45, 1.58s/it]4.05it/
+ 15/49 16G 0.02549 0.01727 0 96 1280: 57%|█████▋ | 36/63 [00:58<00:30, 1.12s/it]4.05it/
+ 15/49 16G 0.02549 0.01713 0 67 1280: 59%|█████▊ | 37/63 [01:01<00:38, 1.50s/it]4.05it/
+ 15/49 16G 0.02548 0.01703 0 78 1280: 60%|██████ | 38/63 [01:02<00:32, 1.31s/it]4.05it/
+ 15/49 16G 0.02554 0.0171 0 105 1280: 62%|██████■| 39/63 [01:03<00:34, 1.42s/it]4.05it/
+ 15/49 16G 0.02535 0.01679 0 80 1280: 68%|██████▊ | 43/63 [01:11<00:23, 1.17s/it]4.05it/
+ 15/49 16G 0.02532 0.01675 0 78 1280: 70%|██████▉ | 44/63 [01:12<00:20, 1.09s/it]4.05it/
+ 15/49 16G 0.02545 0.01677 0 107 1280: 73%|███████▎ | 46/63 [01:15<00:22, 1.29s/it]4.05it/
+ 15/49 16G 0.02546 0.01681 0 106 1280: 75%|███████■| 47/63 [01:17<00:22, 1.42s/it]4.05it/
+ 15/49 16G 0.02527 0.01657 0 96 1280: 83%|████████▎ | 52/63 [01:25<00:11, 1.06s/it]4.05it/
+ 15/49 16G 0.02542 0.01666 0 89 1280: 86%|████████▌ | 54/63 [01:28<00:11, 1.27s/it]4.05it/
+ 15/49 16G 0.02545 0.01661 0 75 1280: 87%|████████▋ | 55/63 [01:30<00:11, 1.40s/it]4.05it/
+ 15/49 16G 0.0254 0.01656 0 80 1280: 94%|█████████▎| 59/63 [01:37<00:04, 1.16s/it]4.05it/
+ 15/49 16G 0.02533 0.01656 0 96 1280: 95%|█████████▌| 60/63 [01:38<00:03, 1.06s/it]4.05it/
+ 15/49 16G 0.02542 0.01657 0 90 1280: 98%|█████████▊| 62/63 [01:41<00:01, 1.26s/it]4.05it/
+ 15/49 16G 0.0254 0.01648 0 33 1280: 100%|██████████| 63/63 [01:41<00:00, 1.62s/it]4.05it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.07it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.07it/
+ 16/49 16G 0.0257 0.01585 0 90 1280: 13%|█▎ | 8/63 [00:10<00:47, 1.16it/s] 4.07it/
+ 16/49 16G 0.02448 0.01514 0 53 1280: 19%|█▉ | 12/63 [00:19<01:00, 1.19s/it]4.07it/
+ 16/49 16G 0.02427 0.01533 0 100 1280: 21%|██ | 13/63 [00:19<00:53, 1.07s/it]4.07it/
+ 16/49 16G 0.02466 0.01505 0 88 1280: 24%|██■| 15/63 [00:23<01:00, 1.25s/it]4.07it/
+ 16/49 16G 0.02489 0.01544 0 113 1280: 25%|██▌ | 16/63 [00:23<00:43, 1.08it/s]4.07it/
+ 16/49 16G 0.02438 0.01625 0 96 1280: 33%|███▎ | 21/63 [00:41<01:05, 1.55s/it]4.07it/
+ 16/49 16G 0.02455 0.01591 0 71 1280: 37%|███▋ | 23/63 [00:46<01:12, 1.82s/it]4.07it/
+ 16/49 16G 0.02438 0.01594 0 93 1280: 38%|███▊ | 24/63 [00:46<00:51, 1.33s/it]4.07it/
+ 16/49 16G 0.02448 0.0161 0 119 1280: 40%|███▉ | 25/63 [00:55<02:22, 3.75s/it]4.07it/
+ 16/49 16G 0.02434 0.01601 0 99 1280: 46%|████▌ | 29/63 [00:56<00:35, 1.05s/it]4.07it/
+ 16/49 16G 0.02429 0.01592 0 92 1280: 51%|█████ | 32/63 [00:59<00:26, 1.17it/s]4.07it/
+ 16/49 16G 0.02429 0.01596 0 96 1280: 57%|█████▋ | 36/63 [01:09<00:33, 1.25s/it]4.07it/
+ 16/49 16G 0.0243 0.01592 0 89 1280: 59%|█████▊ | 37/63 [01:09<00:29, 1.15s/it]4.07it/
+ 16/49 16G 0.02423 0.01609 0 86 1280: 63%|██████▎ | 40/63 [01:12<00:19, 1.16it/s]4.07it/
+ 16/49 16G 0.02419 0.01603 0 75 1280: 65%|██████▌ | 41/63 [01:21<01:13, 3.32s/it]4.07it/
+ 16/49 16G 0.02419 0.01598 0 66 1280: 71%|███████■| 45/63 [01:23<00:20, 1.14s/it]4.07it/
+ 16/49 16G 0.02413 0.01596 0 113 1280: 75%|███████■| 47/63 [01:25<00:18, 1.14s/it]4.07it/
+ 16/49 16G 0.02412 0.0159 0 75 1280: 76%|███████▌ | 48/63 [01:26<00:12, 1.18it/s]4.07it/
+ 16/49 16G 0.02404 0.01589 0 93 1280: 83%|████████▎ | 52/63 [01:35<00:13, 1.26s/it]4.07it/
+ 16/49 16G 0.02406 0.01589 0 83 1280: 84%|████████■| 53/63 [01:36<00:10, 1.10s/it]4.07it/
+ 16/49 16G 0.02405 0.01589 0 119 1280: 89%|████████▉ | 56/63 [01:39<00:05, 1.23it/s]4.07it/
+ 16/49 16G 0.02411 0.01586 0 112 1280: 97%|█████████▋| 61/63 [01:49<00:02, 1.09s/it]4.07it/
+ 16/49 16G 0.024 0.01587 0 55 1280: 100%|██████████| 63/63 [01:52<00:00, 1.78s/it]4.07it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.80it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.80it/
+ 17/49 16G 0.02231 0.01586 0 81 1280: 10%|â–‰ | 6/63 [00:07<00:48, 1.16it/s] 3.80it/
+ 17/49 16G 0.02433 0.01651 0 80 1280: 14%|█■| 9/63 [00:10<00:40, 1.35it/s] 3.80it/
+ 17/49 16G 0.02375 0.01647 0 83 1280: 22%|██■| 14/63 [00:20<00:50, 1.04s/it]3.80it/
+ 17/49 16G 0.02379 0.0168 0 104 1280: 27%|██▋ | 17/63 [00:23<00:38, 1.21it/s]3.80it/
+ 17/49 16G 0.0238 0.01668 0 85 1280: 32%|███■| 20/63 [00:38<01:49, 2.54s/it]3.80it/
+ 17/49 16G 0.02385 0.017 0 123 1280: 38%|███▊ | 24/63 [00:40<00:39, 1.01s/it]3.80it/
+ 17/49 16G 0.02389 0.01686 0 78 1280: 40%|███▉ | 25/63 [00:41<00:34, 1.10it/s]3.80it/
+ 17/49 16G 0.02395 0.01669 0 95 1280: 46%|████▌ | 29/63 [00:56<01:01, 1.82s/it]3.80it/
+ 17/49 16G 0.02381 0.01639 0 116 1280: 52%|█████■| 33/63 [00:57<00:19, 1.57it/s]3.80it/
+ 17/49 16G 0.02374 0.01637 0 123 1280: 60%|██████ | 38/63 [01:08<00:24, 1.04it/s]3.80it/
+ 17/49 16G 0.02369 0.01624 0 65 1280: 63%|██████▎ | 40/63 [01:11<00:24, 1.07s/it]3.80it/
+ 17/49 16G 0.02373 0.01627 0 103 1280: 65%|██████▌ | 41/63 [01:11<00:18, 1.19it/s]3.80it/
+ 17/49 16G 0.02379 0.01627 0 110 1280: 68%|██████▊ | 43/63 [01:21<00:49, 2.48s/it]3.80it/
+ 17/49 16G 0.02387 0.01618 0 78 1280: 73%|███████▎ | 46/63 [01:21<00:16, 1.02it/s]3.80it/
+ 17/49 16G 0.02384 0.01615 0 81 1280: 78%|███████▊ | 49/63 [01:24<00:10, 1.29it/s]3.80it/
+ 17/49 16G 0.02392 0.01607 0 100 1280: 86%|████████▌ | 54/63 [01:35<00:09, 1.04s/it]3.80it/
+ 17/49 16G 0.02385 0.01593 0 81 1280: 90%|█████████ | 57/63 [01:37<00:04, 1.29it/s]3.80it/
+ 17/49 16G 0.02392 0.01599 0 70 1280: 100%|██████████| 63/63 [01:48<00:00, 1.72s/it]3.80it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.01it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.01it/
+ 18/49 16G 0.02339 0.01534 0 91 1280: 16%|█▌ | 10/63 [00:11<00:22, 2.39it/s]4.01it/
+ 18/49 16G 0.02355 0.01504 0 140 1280: 29%|██▊ | 18/63 [00:24<00:21, 2.07it/s]4.01it/
+ 18/49 16G 0.0233 0.01507 0 90 1280: 33%|███▎ | 21/63 [00:42<02:02, 2.93s/it]4.01it/
+ 18/49 16G 0.02335 0.01534 0 110 1280: 40%|███▉ | 25/63 [00:44<00:38, 1.01s/it]4.01it/
+ 18/49 16G 0.02331 0.01516 0 66 1280: 41%|████■| 26/63 [00:46<00:54, 1.46s/it]4.01it/
+ 18/49 16G 0.02312 0.01507 0 70 1280: 43%|████▎ | 27/63 [00:57<02:29, 4.16s/it]4.01it/
+ 18/49 16G 0.02297 0.01525 0 92 1280: 54%|█████■| 34/63 [00:58<00:16, 1.75it/s]4.01it/
+ 18/49 16G 0.02291 0.01533 0 90 1280: 60%|██████ | 38/63 [01:11<00:36, 1.47s/it]4.01it/
+ 18/49 16G 0.02287 0.0155 0 102 1280: 67%|██████▋ | 42/63 [01:12<00:10, 2.03it/s]4.01it/
+ 18/49 16G 0.02289 0.01559 0 89 1280: 79%|███████▉ | 50/63 [01:24<00:06, 2.07it/s]4.01it/
+ 18/49 16G 0.02296 0.01565 0 73 1280: 84%|████████■| 53/63 [01:37<00:20, 2.02s/it]4.01it/
+ 18/49 16G 0.02289 0.01565 0 104 1280: 92%|█████████â–| 58/63 [01:38<00:02, 2.03it/s]4.01it/
+ 18/49 16G 0.0229 0.0157 0 67 1280: 100%|██████████| 63/63 [01:51<00:00, 1.76s/it]4.01it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ 19/49 16G 0.02166 0.01377 0 86 1280: 8%|â–Š | 5/63 [00:09<02:21, 2.45s/it] 4.03it/
+ 19/49 16G 0.0222 0.01501 0 77 1280: 17%|█▋ | 11/63 [00:11<00:21, 2.45it/s]4.03it/
+ 19/49 16G 0.02258 0.01523 0 62 1280: 29%|██▊ | 18/63 [00:23<00:27, 1.63it/s]4.03it/
+ 19/49 16G 0.02244 0.01511 0 86 1280: 30%|███ | 19/63 [00:24<00:21, 2.07it/s]4.03it/
+ 19/49 16G 0.02235 0.01497 0 85 1280: 32%|███■| 20/63 [00:35<02:46, 3.88s/it]4.03it/
+ 19/49 16G 0.02237 0.01514 0 90 1280: 37%|███▋ | 23/63 [00:36<01:01, 1.54s/it]4.03it/
+ 19/49 16G 0.02257 0.01547 0 119 1280: 40%|███▉ | 25/63 [00:39<00:55, 1.45s/it]4.03it/
+ 19/49 16G 0.02246 0.01542 0 91 1280: 41%|████■| 26/63 [00:44<01:23, 2.27s/it]4.03it/
+ 19/49 16G 0.02243 0.01535 0 96 1280: 43%|████▎ | 27/63 [00:45<01:14, 2.07s/it]4.03it/
+ 19/49 16G 0.02228 0.01527 0 103 1280: 46%|████▌ | 29/63 [00:56<01:49, 3.22s/it]4.03it/
+ 19/49 16G 0.02217 0.01535 0 117 1280: 56%|█████▌ | 35/63 [00:57<00:16, 1.73it/s]4.03it/
+ 19/49 16G 0.02219 0.01526 0 77 1280: 57%|█████▋ | 36/63 [01:10<01:55, 4.27s/it]4.03it/
+ 19/49 16G 0.02219 0.01513 0 66 1280: 68%|██████▊ | 43/63 [01:11<00:10, 1.92it/s]4.03it/
+ 19/49 16G 0.02216 0.015 0 74 1280: 81%|████████ | 51/63 [01:24<00:05, 2.07it/s]4.03it/
+ 19/49 16G 0.02215 0.01494 0 78 1280: 84%|████████■| 53/63 [01:36<00:27, 2.79s/it]4.03it/
+ 19/49 16G 0.02198 0.01475 0 75 1280: 94%|█████████▎| 59/63 [01:37<00:01, 2.04it/s]4.03it/
+ 19/49 16G 0.02211 0.01473 0 49 1280: 100%|██████████| 63/63 [01:50<00:00, 1.75s/it]4.03it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.10it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.10it/
+ 20/49 16G 0.02224 0.01367 0 74 1280: 19%|█▉ | 12/63 [00:11<00:20, 2.43it/s]4.10it/
+ 20/49 16G 0.02228 0.014 0 86 1280: 27%|██▋ | 17/63 [00:23<00:49, 1.07s/it]4.10it/
+ 20/49 16G 0.02193 0.01374 0 57 1280: 32%|███■| 20/63 [00:24<00:20, 2.07it/s]4.10it/
+ 20/49 16G 0.02178 0.01369 0 83 1280: 44%|████■| 28/63 [00:37<00:17, 2.05it/s]4.10it/
+ 20/49 16G 0.02178 0.01352 0 84 1280: 51%|█████ | 32/63 [00:57<01:10, 2.26s/it]4.10it/
+ 20/49 16G 0.02168 0.01337 0 73 1280: 57%|█████▋ | 36/63 [01:01<00:35, 1.31s/it]4.10it/
+ 20/49 16G 0.02151 0.01335 0 81 1280: 70%|██████▉ | 44/63 [01:12<00:09, 2.02it/s]4.10it/
+ 20/49 16G 0.02133 0.01333 0 81 1280: 83%|████████▎ | 52/63 [01:24<00:05, 2.19it/s]4.10it/
+ 20/49 16G 0.02134 0.01334 0 102 1280: 89%|████████▉ | 56/63 [01:36<00:10, 1.46s/it]4.10it/
+ 20/49 16G 0.02145 0.01349 0 71 1280: 95%|█████████▌| 60/63 [01:37<00:01, 2.04it/s]4.10it/
+ 20/49 16G 0.0214 0.01358 0 51 1280: 100%|██████████| 63/63 [01:49<00:00, 1.74s/it]4.10it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.08it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.08it/
+ 21/49 16G 0.02179 0.01564 0 95 1280: 13%|█▎ | 8/63 [00:10<01:24, 1.54s/it] 4.08it/
+ 21/49 16G 0.02176 0.0157 0 97 1280: 21%|██ | 13/63 [00:11<00:20, 2.49it/s]4.08it/
+ 21/49 16G 0.0216 0.01509 0 94 1280: 33%|███▎ | 21/63 [00:24<00:20, 2.06it/s]4.08it/
+ 21/49 16G 0.02156 0.01502 0 116 1280: 37%|███▋ | 23/63 [00:36<01:50, 2.77s/it]4.08it/
+ 21/49 16G 0.02129 0.01485 0 122 1280: 46%|████▌ | 29/63 [00:37<00:16, 2.05it/s]4.08it/
+ 21/49 16G 0.02133 0.01484 0 111 1280: 51%|█████ | 32/63 [00:55<01:31, 2.94s/it]4.08it/
+ 21/49 16G 0.02134 0.01474 0 75 1280: 59%|█████▊ | 37/63 [00:57<00:18, 1.38it/s]4.08it/
+ 21/49 16G 0.02131 0.01467 0 64 1280: 62%|██████■| 39/63 [01:10<01:12, 3.01s/it]4.08it/
+ 21/49 16G 0.02116 0.01462 0 66 1280: 71%|███████■| 45/63 [01:11<00:09, 1.85it/s]4.08it/
+ 21/49 16G 0.02111 0.01453 0 86 1280: 81%|████████ | 51/63 [01:24<00:09, 1.24it/s]4.08it/
+ 21/49 16G 0.02108 0.01453 0 69 1280: 84%|████████■| 53/63 [01:24<00:04, 2.05it/s]4.08it/
+ 21/49 16G 0.02111 0.01444 0 102 1280: 90%|█████████ | 57/63 [01:36<00:08, 1.43s/it]4.08it/
+ 21/49 16G 0.02105 0.0144 0 68 1280: 97%|█████████▋| 61/63 [01:37<00:01, 1.99it/s]4.08it/
+ 21/49 16G 0.02103 0.01447 0 69 1280: 100%|██████████| 63/63 [01:49<00:00, 1.74s/it]4.08it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.05it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.05it/
+ 22/49 16G 0.02079 0.01496 0 67 1280: 22%|██■| 14/63 [00:11<00:20, 2.41it/s]4.05it/
+ 22/49 16G 0.02061 0.01493 0 101 1280: 29%|██▊ | 18/63 [00:24<01:04, 1.44s/it]4.05it/
+ 22/49 16G 0.02052 0.01468 0 92 1280: 35%|███■| 22/63 [00:25<00:19, 2.06it/s]4.05it/
+ 22/49 16G 0.02014 0.01441 0 99 1280: 48%|████▊ | 30/63 [00:38<00:16, 2.05it/s]4.05it/
+ 22/49 16G 0.02029 0.01449 0 107 1280: 54%|█████■| 34/63 [00:50<00:41, 1.45s/it]4.05it/
+ 22/49 16G 0.02024 0.0146 0 79 1280: 60%|██████ | 38/63 [00:51<00:12, 2.04it/s]4.05it/
+ 22/49 16G 0.02013 0.01445 0 90 1280: 73%|███████▎ | 46/63 [01:10<00:11, 1.52it/s]4.05it/
+ 22/49 16G 0.02016 0.01444 0 101 1280: 84%|████████■| 53/63 [01:24<00:06, 1.49it/s]4.05it/
+ 22/49 16G 0.02012 0.01442 0 90 1280: 86%|████████▌ | 54/63 [01:24<00:04, 1.91it/s]4.05it/
+ 22/49 16G 0.02014 0.01441 0 73 1280: 90%|█████████ | 57/63 [01:36<00:11, 1.96s/it]4.05it/
+ 22/49 16G 0.0203 0.01447 0 110 1280: 98%|█████████▊| 62/63 [01:37<00:00, 2.01it/s]4.05it/
+ 22/49 16G 0.02029 0.01449 0 53 1280: 100%|██████████| 63/63 [01:43<00:00, 1.65s/it]4.05it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+ 23/49 16G 0.01987 0.01402 0 111 1280: 19%|█▉ | 12/63 [00:11<00:41, 1.24it/s]4.02it/
+ 23/49 16G 0.02016 0.01415 0 73 1280: 24%|██■| 15/63 [00:15<00:45, 1.05it/s]4.02it/
+ 23/49 16G 0.02017 0.0139 0 72 1280: 32%|███■| 20/63 [00:24<00:38, 1.13it/s]4.02it/
+ 23/49 16G 0.01989 0.01393 0 110 1280: 35%|███■| 22/63 [00:28<00:54, 1.33s/it]4.02it/
+ 23/49 16G 0.01983 0.01389 0 77 1280: 37%|███▋ | 23/63 [00:28<00:39, 1.02it/s]4.02it/
+ 23/49 16G 0.01999 0.01399 0 103 1280: 44%|████■| 28/63 [00:37<00:31, 1.11it/s]4.02it/
+ 23/49 16G 0.01961 0.0139 0 90 1280: 49%|████▉ | 31/63 [00:41<00:31, 1.02it/s]4.02it/
+ 23/49 16G 0.01971 0.0139 0 85 1280: 56%|█████▌ | 35/63 [00:50<00:33, 1.19s/it]4.02it/
+ 23/49 16G 0.01977 0.01412 0 131 1280: 57%|█████▋ | 36/63 [00:50<00:24, 1.11it/s]4.02it/
+ 23/49 16G 0.0197 0.01406 0 69 1280: 59%|█████▊ | 37/63 [00:54<00:44, 1.72s/it]4.02it/
+ 23/49 16G 0.01961 0.01405 0 106 1280: 62%|██████■| 39/63 [00:54<00:23, 1.03it/s]4.02it/
+ 23/49 16G 0.01958 0.01399 0 86 1280: 63%|██████▎ | 40/63 [01:07<01:39, 4.34s/it]4.02it/
+ 23/49 16G 0.0195 0.01382 0 58 1280: 70%|██████▉ | 44/63 [01:10<00:28, 1.50s/it]4.02it/
+ 23/49 16G 0.01948 0.01377 0 75 1280: 73%|███████▎ | 46/63 [01:12<00:21, 1.25s/it]4.02it/
+ 23/49 16G 0.01948 0.01371 0 72 1280: 75%|███████■| 47/63 [01:12<00:14, 1.07it/s]4.02it/
+ 23/49 16G 0.01944 0.01373 0 96 1280: 83%|████████▎ | 52/63 [01:22<00:10, 1.02it/s]4.02it/
+ 23/49 16G 0.01948 0.01388 0 85 1280: 87%|████████▋ | 55/63 [01:26<00:07, 1.02it/s]4.02it/
+ 23/49 16G 0.01946 0.01386 0 71 1280: 95%|█████████▌| 60/63 [01:36<00:02, 1.07it/s]4.02it/
+ 23/49 16G 0.01945 0.01383 0 48 1280: 100%|██████████| 63/63 [01:40<00:00, 1.60s/it]4.02it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.04it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.04it/
+ 24/49 16G 0.01791 0.01275 0 58 1280: 8%|â–Š | 5/63 [00:05<00:35, 1.62it/s] 4.04it/
+ 24/49 16G 0.02079 0.01337 0 90 1280: 13%|█▎ | 8/63 [00:10<00:55, 1.01s/it] 4.04it/
+ 24/49 16G 0.01984 0.0132 0 90 1280: 21%|██ | 13/63 [00:19<00:43, 1.15it/s]4.04it/
+ 24/49 16G 0.02003 0.01332 0 69 1280: 24%|██■| 15/63 [00:23<01:10, 1.46s/it]4.04it/
+ 24/49 16G 0.02005 0.01346 0 104 1280: 25%|██▌ | 16/63 [00:24<00:50, 1.08s/it]4.04it/
+ 24/49 16G 0.01999 0.01366 0 105 1280: 30%|███ | 19/63 [00:31<01:08, 1.56s/it]4.04it/
+ 24/49 16G 0.02003 0.01386 0 122 1280: 33%|███▎ | 21/63 [00:32<00:36, 1.15it/s]4.04it/
+ 24/49 16G 0.01982 0.01394 0 93 1280: 38%|███▊ | 24/63 [00:37<00:41, 1.08s/it]4.04it/
+ 24/49 16G 0.01977 0.01405 0 117 1280: 46%|████▌ | 29/63 [00:45<00:29, 1.16it/s]4.04it/
+ 24/49 16G 0.01974 0.01402 0 86 1280: 48%|████▊ | 30/63 [00:49<01:01, 1.87s/it]4.04it/
+ 24/49 16G 0.01973 0.014 0 113 1280: 51%|█████ | 32/63 [00:50<00:33, 1.08s/it]4.04it/
+ 24/49 16G 0.01972 0.01398 0 96 1280: 59%|█████▊ | 37/63 [00:58<00:22, 1.16it/s]4.04it/
+ 24/49 16G 0.01957 0.01397 0 87 1280: 63%|██████▎ | 40/63 [01:03<00:24, 1.08s/it]4.04it/
+ 24/49 16G 0.01964 0.01406 0 99 1280: 70%|██████▉ | 44/63 [01:11<00:21, 1.14s/it]4.04it/
+ 24/49 16G 0.01963 0.01409 0 103 1280: 71%|███████■| 45/63 [01:11<00:15, 1.16it/s]4.04it/
+ 24/49 16G 0.01949 0.01408 0 103 1280: 76%|███████▌ | 48/63 [01:21<00:26, 1.74s/it]4.04it/
+ 24/49 16G 0.01943 0.01405 0 81 1280: 79%|███████▉ | 50/63 [01:31<00:39, 3.07s/it]4.04it/
+ 24/49 16G 0.01938 0.01405 0 111 1280: 84%|████████■| 53/63 [01:32<00:12, 1.24s/it]4.04it/
+ 24/49 16G 0.01931 0.01395 0 70 1280: 89%|████████▉ | 56/63 [01:36<00:08, 1.15s/it]4.04it/
+ 24/49 16G 0.01927 0.01381 0 93 1280: 97%|█████████▋| 61/63 [01:45<00:01, 1.15it/s]4.04it/
+ 24/49 16G 0.01924 0.01372 0 38 1280: 100%|██████████| 63/63 [01:49<00:00, 1.74s/it]4.04it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ 25/49 16G 0.01472 0.01211 0 85 1280: 10%|â–‰ | 6/63 [00:05<00:29, 1.91it/s] 4.03it/
+ 25/49 16G 0.01551 0.0126 0 64 1280: 14%|█■| 9/63 [00:11<00:59, 1.10s/it] 4.03it/
+ 25/49 16G 0.01725 0.01299 0 97 1280: 22%|██■| 14/63 [00:18<00:40, 1.21it/s]4.03it/
+ 25/49 16G 0.01673 0.01276 0 83 1280: 27%|██▋ | 17/63 [00:24<00:51, 1.13s/it]4.03it/
+ 25/49 16G 0.01706 0.01302 0 102 1280: 32%|███■| 20/63 [00:31<01:05, 1.51s/it]4.03it/
+ 25/49 16G 0.01731 0.01307 0 74 1280: 35%|███■| 22/63 [00:31<00:34, 1.19it/s]4.03it/
+ 25/49 16G 0.01706 0.01288 0 80 1280: 40%|███▉ | 25/63 [00:37<00:42, 1.11s/it]4.03it/
+ 25/49 16G 0.01726 0.01311 0 99 1280: 48%|████▊ | 30/63 [00:45<00:27, 1.19it/s]4.03it/
+ 25/49 16G 0.0172 0.01308 0 68 1280: 52%|█████■| 33/63 [00:50<00:33, 1.11s/it]4.03it/
+ 25/49 16G 0.01729 0.01305 0 80 1280: 56%|█████▌ | 35/63 [00:57<00:58, 2.08s/it]4.03it/
+ 25/49 16G 0.01737 0.01294 0 98 1280: 60%|██████ | 38/63 [00:58<00:21, 1.19it/s]4.03it/
+ 25/49 16G 0.01732 0.01283 0 88 1280: 65%|██████▌ | 41/63 [01:03<00:24, 1.11s/it]4.03it/
+ 25/49 16G 0.01746 0.01306 0 116 1280: 73%|███████▎ | 46/63 [01:11<00:14, 1.19it/s]4.03it/
+ 25/49 16G 0.01746 0.01302 0 80 1280: 75%|███████■| 47/63 [01:16<00:33, 2.06s/it]4.03it/
+ 25/49 16G 0.01748 0.01304 0 93 1280: 76%|███████▌ | 48/63 [01:22<00:48, 3.23s/it]4.03it/
+ 25/49 16G 0.0175 0.01307 0 98 1280: 78%|███████▊ | 49/63 [01:26<00:48, 3.44s/it]4.03it/
+ 25/49 16G 0.01754 0.01321 0 98 1280: 84%|████████■| 53/63 [01:31<00:14, 1.48s/it]4.03it/
+ 25/49 16G 0.01755 0.0132 0 91 1280: 86%|████████▌ | 54/63 [01:32<00:10, 1.19s/it]4.03it/
+ 25/49 16G 0.01754 0.01306 0 69 1280: 89%|████████▉ | 56/63 [01:40<00:15, 2.26s/it]4.03it/
+ 25/49 16G 0.01756 0.01307 0 100 1280: 90%|█████████ | 57/63 [01:40<00:10, 1.67s/it]4.03it/
+ 25/49 16G 0.01756 0.01292 0 30 1280: 100%|██████████| 63/63 [01:48<00:00, 1.73s/it]4.03it/
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:01<00:00, 4.00it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.06it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.06it/
+ 26/49 16G 0.01755 0.01277 0 82 1280: 16%|█▌ | 10/63 [00:09<00:21, 2.44it/s]4.06it/
+ 26/49 16G 0.01745 0.01291 0 89 1280: 29%|██▊ | 18/63 [00:23<00:22, 2.04it/s]4.06it/
+ 26/49 16G 0.01739 0.01291 0 117 1280: 33%|███▎ | 21/63 [00:35<01:23, 1.98s/it]4.06it/
+ 26/49 16G 0.01735 0.01275 0 93 1280: 41%|████■| 26/63 [00:36<00:18, 1.99it/s]4.06it/
+ 26/49 16G 0.01729 0.0127 0 103 1280: 52%|█████■| 33/63 [00:49<00:19, 1.54it/s]4.06it/
+ 26/49 16G 0.01731 0.01278 0 108 1280: 54%|█████■| 34/63 [00:49<00:14, 1.97it/s]4.06it/
+ 26/49 16G 0.01733 0.01295 0 93 1280: 57%|█████▋ | 36/63 [01:01<01:14, 2.76s/it]4.06it/
+ 26/49 16G 0.01725 0.01272 0 93 1280: 67%|██████▋ | 42/63 [01:02<00:10, 1.95it/s]4.06it/
+ 26/49 16G 0.01721 0.01271 0 113 1280: 76%|███████▌ | 48/63 [01:15<00:12, 1.18it/s]4.06it/
+ 26/49 16G 0.01722 0.01279 0 109 1280: 79%|███████▉ | 50/63 [01:15<00:06, 1.97it/s]4.06it/
+ 26/49 16G 0.0172 0.0127 0 82 1280: 83%|████████▎ | 52/63 [01:33<00:44, 4.09s/it]4.06it/
+ 26/49 16G 0.01719 0.01273 0 90 1280: 86%|████████▌ | 54/63 [01:35<00:21, 2.37s/it]4.06it/
+ 26/49 16G 0.01719 0.0126 0 57 1280: 92%|█████████â–| 58/63 [01:36<00:03, 1.29it/s]4.06it/
+ 26/49 16G 0.01719 0.01264 0 99 1280: 95%|█████████▌| 60/63 [01:48<00:08, 2.81s/it]4.06it/
+ 26/49 16G 0.01719 0.01262 0 55 1280: 100%|██████████| 63/63 [01:48<00:00, 1.73s/it]4.06it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.79it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.79it/
+ 27/49 16G 0.01904 0.01133 0 86 1280: 5%|â– | 3/63 [00:02<00:33, 1.81it/s] 2.79it/
+ 27/49 16G 0.01743 0.01194 0 72 1280: 14%|█■| 9/63 [00:08<00:33, 1.62it/s] 2.79it/
+ 27/49 16G 0.0178 0.01227 0 100 1280: 17%|█▋ | 11/63 [00:09<00:20, 2.56it/s]2.79it/
+ 27/49 16G 0.01794 0.01252 0 94 1280: 19%|█▉ | 12/63 [00:20<03:14, 3.81s/it]2.79it/
+ 27/49 16G 0.01766 0.01317 0 81 1280: 30%|███ | 19/63 [00:22<00:24, 1.81it/s]2.79it/
+ 27/49 16G 0.01773 0.01309 0 99 1280: 37%|███▋ | 23/63 [00:34<00:57, 1.45s/it]2.79it/
+ 27/49 16G 0.01768 0.01295 0 73 1280: 43%|████▎ | 27/63 [00:35<00:19, 1.83it/s]2.79it/
+ 27/49 16G 0.01772 0.01307 0 124 1280: 52%|█████■| 33/63 [00:48<00:28, 1.06it/s]2.79it/
+ 27/49 16G 0.01772 0.01317 0 111 1280: 56%|█████▌ | 35/63 [00:49<00:15, 1.81it/s]2.79it/
+ 27/49 16G 0.01781 0.01305 0 71 1280: 60%|██████ | 38/63 [01:00<00:48, 1.95s/it]2.79it/
+ 27/49 16G 0.01773 0.01295 0 87 1280: 68%|██████▊ | 43/63 [01:02<00:10, 1.83it/s]2.79it/
+ 27/49 16G 0.01766 0.01285 0 88 1280: 78%|███████▊ | 49/63 [01:14<00:12, 1.09it/s]2.79it/
+ 27/49 16G 0.01777 0.0128 0 86 1280: 81%|████████ | 51/63 [01:15<00:06, 1.85it/s]2.79it/
+ 27/49 16G 0.0178 0.01275 0 79 1280: 84%|████████■| 53/63 [01:26<00:27, 2.71s/it]2.79it/
+ 27/49 16G 0.01772 0.0126 0 72 1280: 94%|█████████▎| 59/63 [01:28<00:02, 1.84it/s]2.79it/
+ 27/49 16G 0.01771 0.01267 0 54 1280: 100%|██████████| 63/63 [01:47<00:00, 1.71s/it]2.79it/
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:01<00:00, 3.52it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.04it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.04it/
+ 28/49 16G 0.01416 0.01092 0 108 1280: 6%|â–‹ | 4/63 [00:02<00:40, 1.44it/s] 3.04it/
+ 28/49 16G 0.01658 0.01184 0 84 1280: 19%|█▉ | 12/63 [00:10<00:18, 2.72it/s]3.04it/
+ 28/49 16G 0.01645 0.01207 0 103 1280: 24%|██■| 15/63 [00:22<01:34, 1.96s/it]3.04it/
+ 28/49 16G 0.01632 0.01198 0 87 1280: 32%|███■| 20/63 [00:23<00:20, 2.08it/s]3.04it/
+ 28/49 16G 0.01635 0.01234 0 103 1280: 41%|████■| 26/63 [00:37<00:30, 1.21it/s]3.04it/
+ 28/49 16G 0.01638 0.01224 0 76 1280: 44%|████■| 28/63 [00:37<00:17, 2.01it/s]3.04it/
+ 28/49 16G 0.01644 0.01226 0 139 1280: 57%|█████▋ | 36/63 [00:50<00:13, 2.04it/s]3.04it/
+ 28/49 16G 0.01665 0.01235 0 89 1280: 63%|██████▎ | 40/63 [01:02<00:33, 1.46s/it]3.04it/
+ 28/49 16G 0.01662 0.01237 0 129 1280: 70%|██████▉ | 44/63 [01:03<00:09, 2.04it/s]3.04it/
+ 28/49 16G 0.01675 0.01257 0 88 1280: 83%|████████▎ | 52/63 [01:16<00:05, 2.04it/s]3.04it/
+ 28/49 16G 0.01674 0.01257 0 101 1280: 84%|████████■| 53/63 [01:28<00:39, 3.97s/it]3.04it/
+ 28/49 16G 0.01681 0.01255 0 93 1280: 95%|█████████▌| 60/63 [01:30<00:01, 2.02it/s]3.04it/
+ 28/49 16G 0.01681 0.01255 0 92 1280: 98%|█████████▊| 62/63 [01:48<00:04, 4.10s/it]3.04it/
+ 28/49 16G 0.01682 0.0125 0 32 1280: 100%|██████████| 63/63 [01:48<00:00, 1.73s/it]3.04it/
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:03<00:01, 1.92it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.75it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:04<00:00, 1.75it/
+ 29/49 16G 0.01561 0.01334 0 69 1280: 8%|â–Š | 5/63 [00:02<00:20, 2.81it/s] 1.75it/
+ 29/49 16G 0.0153 0.01351 0 114 1280: 10%|â–‰ | 6/63 [00:09<02:27, 2.58s/it] 1.75it/
+ 29/49 16G 0.01618 0.01299 0 78 1280: 21%|██ | 13/63 [00:10<00:18, 2.76it/s]1.75it/
+ 29/49 16G 0.01645 0.01263 0 89 1280: 33%|███▎ | 21/63 [00:22<00:19, 2.17it/s]1.75it/
+ 29/49 16G 0.01653 0.01253 0 72 1280: 40%|███▉ | 25/63 [00:35<00:55, 1.47s/it]1.75it/
+ 29/49 16G 0.01641 0.01263 0 116 1280: 46%|████▌ | 29/63 [00:36<00:16, 2.03it/s]1.75it/
+ 29/49 16G 0.0164 0.01258 0 93 1280: 59%|█████▊ | 37/63 [00:49<00:12, 2.04it/s]1.75it/
+ 29/49 16G 0.01636 0.0125 0 67 1280: 62%|██████■| 39/63 [01:01<01:06, 2.79s/it]1.75it/
+ 29/49 16G 0.01635 0.01233 0 73 1280: 71%|███████■| 45/63 [01:02<00:08, 2.04it/s]1.75it/
+ 29/49 16G 0.01636 0.01228 0 79 1280: 78%|███████▊ | 49/63 [01:15<00:21, 1.51s/it]1.75it/
+ 29/49 16G 0.01629 0.01222 0 106 1280: 84%|████████■| 53/63 [01:16<00:05, 1.99it/s]1.75it/
+ 29/49 16G 0.01634 0.01251 0 129 1280: 97%|█████████▋| 61/63 [01:29<00:00, 2.08it/s]1.75it/
+ 29/49 16G 0.01627 0.01245 0 40 1280: 100%|██████████| 63/63 [01:46<00:00, 1.69s/it]1.75it/
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:03<00:01, 1.77it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.18it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.18it/
+ 30/49 16G 0.01544 0.01133 0 97 1280: 8%|â–Š | 5/63 [00:05<00:38, 1.50it/s] 2.18it/
+ 30/49 16G 0.01545 0.01105 0 73 1280: 10%|â–‰ | 6/63 [00:08<01:25, 1.51s/it] 2.18it/
+ 30/49 16G 0.01527 0.01147 0 84 1280: 22%|██■| 14/63 [00:11<00:14, 3.33it/s]2.18it/
+ 30/49 16G 0.01536 0.01185 0 129 1280: 27%|██▋ | 17/63 [00:24<01:30, 1.97s/it]2.18it/
+ 30/49 16G 0.0155 0.01189 0 90 1280: 35%|███■| 22/63 [00:25<00:19, 2.07it/s]2.18it/
+ 30/49 16G 0.01553 0.01162 0 101 1280: 46%|████▌ | 29/63 [00:38<00:21, 1.59it/s]2.18it/
+ 30/49 16G 0.01557 0.01163 0 87 1280: 48%|████▊ | 30/63 [00:38<00:16, 2.03it/s]2.18it/
+ 30/49 16G 0.01568 0.01181 0 98 1280: 60%|██████ | 38/63 [00:51<00:12, 2.03it/s]2.18it/
+ 30/49 16G 0.01586 0.01204 0 78 1280: 68%|██████▊ | 43/63 [01:04<00:21, 1.09s/it]2.18it/
+ 30/49 16G 0.01579 0.01197 0 103 1280: 73%|███████▎ | 46/63 [01:04<00:08, 2.03it/s]2.18it/
+ 30/49 16G 0.0158 0.01198 0 76 1280: 86%|████████▌ | 54/63 [01:18<00:04, 2.04it/s]2.18it/
+ 30/49 16G 0.01581 0.01201 0 100 1280: 89%|████████▉ | 56/63 [01:30<00:19, 2.84s/it]2.18it/
+ 30/49 16G 0.01575 0.01201 0 91 1280: 98%|█████████▊| 62/63 [01:31<00:00, 2.02it/s]2.18it/
+ 30/49 16G 0.01573 0.01204 0 59 1280: 100%|██████████| 63/63 [01:37<00:00, 1.54s/it]2.18it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.93it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.93it/
+ 31/49 16G 0.01608 0.01262 0 107 1280: 10%|â–‰ | 6/63 [00:02<00:33, 1.72it/s] 3.93it/
+ 31/49 16G 0.01556 0.01266 0 106 1280: 11%|â–ˆ | 7/63 [00:05<01:20, 1.44s/it] 3.93it/
+ 31/49 16G 0.01586 0.01293 0 111 1280: 14%|█■| 9/63 [00:17<02:51, 3.18s/it] 3.93it/
+ 31/49 16G 0.01581 0.01279 0 77 1280: 19%|█▉ | 12/63 [00:17<01:03, 1.25s/it]3.93it/
+ 31/49 16G 0.01567 0.01255 0 90 1280: 24%|██■| 15/63 [00:23<01:02, 1.31s/it]3.93it/
+ 31/49 16G 0.01547 0.0123 0 79 1280: 30%|███ | 19/63 [00:33<01:02, 1.42s/it]3.93it/
+ 31/49 16G 0.01551 0.01245 0 104 1280: 32%|███■| 20/63 [00:33<00:45, 1.06s/it]3.93it/
+ 31/49 16G 0.01546 0.01237 0 102 1280: 37%|███▋ | 23/63 [00:37<00:42, 1.05s/it]3.93it/
+ 31/49 16G 0.01544 0.0124 0 103 1280: 40%|███▉ | 25/63 [00:45<01:23, 2.19s/it]3.93it/
+ 31/49 16G 0.01547 0.0124 0 95 1280: 44%|████■| 28/63 [00:46<00:38, 1.09s/it]3.93it/
+ 31/49 16G 0.0154 0.01221 0 50 1280: 49%|████▉ | 31/63 [00:50<00:35, 1.10s/it]3.93it/
+ 31/49 16G 0.01546 0.01222 0 93 1280: 54%|█████■| 34/63 [00:58<00:46, 1.60s/it]3.93it/
+ 31/49 16G 0.01542 0.01233 0 95 1280: 57%|█████▋ | 36/63 [01:00<00:28, 1.07s/it]3.93it/
+ 31/49 16G 0.01541 0.0123 0 79 1280: 60%|██████ | 38/63 [01:03<00:30, 1.22s/it]3.93it/
+ 31/49 16G 0.01541 0.01232 0 108 1280: 62%|██████■| 39/63 [01:04<00:25, 1.08s/it]3.93it/
+ 31/49 16G 0.01543 0.01223 0 115 1280: 70%|██████▉ | 44/63 [01:13<00:19, 1.03s/it]3.93it/
+ 31/49 16G 0.01554 0.01218 0 68 1280: 75%|███████■| 47/63 [01:17<00:17, 1.08s/it]3.93it/
+ 31/49 16G 0.01551 0.01211 0 98 1280: 79%|███████▉ | 50/63 [01:25<00:20, 1.61s/it]3.93it/
+ 31/49 16G 0.01547 0.01207 0 84 1280: 83%|████████▎ | 52/63 [01:26<00:11, 1.04s/it]3.93it/
+ 31/49 16G 0.01541 0.01207 0 89 1280: 87%|████████▋ | 55/63 [01:30<00:08, 1.09s/it]3.93it/
+ 31/49 16G 0.01547 0.01203 0 61 1280: 94%|█████████▎| 59/63 [01:39<00:05, 1.32s/it]3.93it/
+ 31/49 16G 0.01548 0.01201 0 82 1280: 95%|█████████▌| 60/63 [01:39<00:03, 1.03s/it]3.93it/
+ 31/49 16G 0.01541 0.01194 0 49 1280: 100%|██████████| 63/63 [01:43<00:00, 1.64s/it]3.93it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 4.00it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 4.00it/
+ 32/49 16G 0.0152 0.0123 0 89 1280: 2%|â– | 1/63 [00:05<05:44, 5.55s/it] 4.00it/
+ 32/49 16G 0.01287 0.01065 0 93 1280: 8%|â–Š | 5/63 [00:07<00:49, 1.17it/s] 4.00it/
+ 32/49 16G 0.01552 0.01129 0 81 1280: 13%|█▎ | 8/63 [00:10<00:45, 1.21it/s] 4.00it/
+ 32/49 16G 0.01475 0.0109 0 98 1280: 21%|██ | 13/63 [00:25<01:05, 1.32s/it]4.00it/
+ 32/49 16G 0.01497 0.01122 0 116 1280: 22%|██■| 14/63 [00:31<02:22, 2.92s/it]4.00it/
+ 32/49 16G 0.01528 0.01132 0 84 1280: 25%|██▌ | 16/63 [00:32<01:14, 1.59s/it]4.00it/
+ 32/49 16G 0.01506 0.01177 0 106 1280: 33%|███▎ | 21/63 [00:41<00:41, 1.02it/s]4.00it/
+ 32/49 16G 0.01508 0.01166 0 88 1280: 37%|███▋ | 23/63 [00:44<00:45, 1.15s/it]4.00it/
+ 32/49 16G 0.01508 0.01163 0 83 1280: 38%|███▊ | 24/63 [00:44<00:33, 1.17it/s]4.00it/
+ 32/49 16G 0.015 0.01168 0 116 1280: 46%|████▌ | 29/63 [00:53<00:35, 1.04s/it]4.00it/
+ 32/49 16G 0.01489 0.01134 0 62 1280: 51%|█████ | 32/63 [00:57<00:29, 1.04it/s]4.00it/
+ 32/49 16G 0.01497 0.01133 0 63 1280: 56%|█████▌ | 35/63 [01:06<00:45, 1.61s/it]4.00it/
+ 32/49 16G 0.01492 0.01137 0 95 1280: 59%|█████▊ | 37/63 [01:07<00:27, 1.04s/it]4.00it/
+ 32/49 16G 0.0149 0.0114 0 98 1280: 60%|██████ | 38/63 [01:10<00:39, 1.58s/it]4.00it/
+ 32/49 16G 0.01493 0.01145 0 104 1280: 63%|██████▎ | 40/63 [01:10<00:22, 1.03it/s]4.00it/
+ 32/49 16G 0.01497 0.0114 0 66 1280: 70%|██████▉ | 44/63 [01:20<00:26, 1.38s/it]4.00it/
+ 32/49 16G 0.01497 0.01142 0 105 1280: 71%|███████■| 45/63 [01:20<00:18, 1.03s/it]4.00it/
+ 32/49 16G 0.01488 0.01144 0 111 1280: 76%|███████▌ | 48/63 [01:24<00:14, 1.03it/s]4.00it/
+ 32/49 16G 0.01488 0.01145 0 71 1280: 79%|███████▉ | 50/63 [01:32<00:28, 2.22s/it]4.00it/
+ 32/49 16G 0.01494 0.01154 0 102 1280: 84%|████████■| 53/63 [01:33<00:10, 1.03s/it]4.00it/
+ 32/49 16G 0.01491 0.01152 0 93 1280: 86%|████████▌ | 54/63 [01:36<00:13, 1.55s/it]4.00it/
+ 32/49 16G 0.01488 0.01155 0 78 1280: 89%|████████▉ | 56/63 [01:37<00:06, 1.04it/s]4.00it/
+ 32/49 16G 0.01491 0.01156 0 105 1280: 94%|█████████▎| 59/63 [01:45<00:06, 1.65s/it]4.00it/
+ 32/49 16G 0.01497 0.01157 0 81 1280: 97%|█████████▋| 61/63 [01:46<00:01, 1.01it/s]4.00it/
+ 32/49 16G 0.01492 0.01152 0 51 1280: 100%|██████████| 63/63 [01:49<00:00, 1.74s/it]4.00it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.07it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.07it/
+ 33/49 16G 0.01141 0.009245 0 96 1280: 10%|â–‰ | 6/63 [00:07<00:46, 1.22it/s] 4.07it/
+ 33/49 16G 0.01324 0.0102 0 87 1280: 14%|█■| 9/63 [00:10<00:41, 1.29it/s] 4.07it/
+ 33/49 16G 0.01343 0.01035 0 106 1280: 22%|██■| 14/63 [00:25<01:10, 1.43s/it]4.07it/
+ 33/49 16G 0.01414 0.01087 0 97 1280: 27%|██▋ | 17/63 [00:26<00:29, 1.55it/s]4.07it/
+ 33/49 16G 0.01429 0.01083 0 112 1280: 35%|███■| 22/63 [00:40<00:49, 1.21s/it]4.07it/
+ 33/49 16G 0.0143 0.0109 0 116 1280: 40%|███▉ | 25/63 [00:42<00:27, 1.36it/s]4.07it/
+ 33/49 16G 0.01423 0.01085 0 86 1280: 41%|████■| 26/63 [00:52<02:14, 3.64s/it]4.07it/
+ 33/49 16G 0.01438 0.01096 0 80 1280: 48%|████▊ | 30/63 [00:53<00:36, 1.11s/it]4.07it/
+ 33/49 16G 0.01428 0.01105 0 86 1280: 52%|█████■| 33/63 [00:56<00:24, 1.24it/s]4.07it/
+ 33/49 16G 0.01438 0.01113 0 73 1280: 57%|█████▋ | 36/63 [01:05<00:45, 1.68s/it]4.07it/
+ 33/49 16G 0.0144 0.01105 0 62 1280: 60%|██████ | 38/63 [01:07<00:28, 1.15s/it]4.07it/
+ 33/49 16G 0.01431 0.01102 0 87 1280: 65%|██████▌ | 41/63 [01:09<00:18, 1.22it/s]4.07it/
+ 33/49 16G 0.01426 0.01099 0 86 1280: 67%|██████▋ | 42/63 [01:18<01:08, 3.24s/it]4.07it/
+ 33/49 16G 0.01432 0.01096 0 51 1280: 73%|███████▎ | 46/63 [01:20<00:19, 1.14s/it]4.07it/
+ 33/49 16G 0.01425 0.01092 0 80 1280: 75%|███████■| 47/63 [01:22<00:23, 1.46s/it]4.07it/
+ 33/49 16G 0.0142 0.01102 0 122 1280: 78%|███████▊ | 49/63 [01:23<00:11, 1.24it/s]4.07it/
+ 33/49 16G 0.01422 0.011 0 72 1280: 83%|████████▎ | 52/63 [01:32<00:18, 1.67s/it]4.07it/
+ 33/49 16G 0.01429 0.01097 0 69 1280: 86%|████████▌ | 54/63 [01:33<00:10, 1.13s/it]4.07it/
+ 33/49 16G 0.01423 0.01108 0 105 1280: 90%|█████████ | 57/63 [01:36<00:04, 1.24it/s]4.07it/
+ 33/49 16G 0.0142 0.01118 0 92 1280: 97%|█████████▋| 61/63 [01:46<00:03, 1.50s/it]4.07it/
+ 33/49 16G 0.01415 0.01118 0 50 1280: 100%|██████████| 63/63 [01:47<00:00, 1.70s/it]4.07it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.04it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.04it/
+ 34/49 16G 0.01365 0.01108 0 96 1280: 14%|█■| 9/63 [00:11<00:34, 1.58it/s] 4.04it/
+ 34/49 16G 0.01372 0.01089 0 71 1280: 16%|█▌ | 10/63 [00:11<00:26, 2.03it/s]4.04it/
+ 34/49 16G 0.01393 0.011 0 114 1280: 21%|██ | 13/63 [00:22<01:32, 1.85s/it]4.04it/
+ 34/49 16G 0.01396 0.01122 0 81 1280: 24%|██■| 15/63 [00:25<01:11, 1.48s/it]4.04it/
+ 34/49 16G 0.01391 0.01137 0 103 1280: 29%|██▊ | 18/63 [00:28<00:46, 1.03s/it]4.04it/
+ 34/49 16G 0.01388 0.01126 0 81 1280: 41%|████■| 26/63 [00:43<00:21, 1.73it/s]4.04it/
+ 34/49 16G 0.01394 0.01127 0 84 1280: 46%|████▌ | 29/63 [00:55<01:06, 1.97s/it]4.04it/
+ 34/49 16G 0.01397 0.01105 0 93 1280: 54%|█████■| 34/63 [00:57<00:16, 1.72it/s]4.04it/
+ 34/49 16G 0.01398 0.0111 0 97 1280: 56%|█████▌ | 35/63 [01:07<01:35, 3.41s/it]4.04it/
+ 34/49 16G 0.01396 0.01118 0 124 1280: 59%|█████▊ | 37/63 [01:07<00:45, 1.76s/it]4.04it/
+ 34/49 16G 0.01396 0.01104 0 93 1280: 67%|██████▋ | 42/63 [01:10<00:12, 1.67it/s]4.04it/
+ 34/49 16G 0.01397 0.01112 0 95 1280: 71%|███████■| 45/63 [01:21<00:31, 1.75s/it]4.04it/
+ 34/49 16G 0.01395 0.01115 0 115 1280: 73%|███████▎ | 46/63 [01:23<00:32, 1.91s/it]4.04it/
+ 34/49 16G 0.01395 0.01113 0 93 1280: 79%|███████▉ | 50/63 [01:24<00:07, 1.67it/s]4.04it/
+ 34/49 16G 0.01402 0.01112 0 73 1280: 84%|████████■| 53/63 [01:34<00:17, 1.74s/it]4.04it/
+ 34/49 16G 0.014 0.01112 0 93 1280: 92%|█████████â–| 58/63 [01:37<00:02, 1.69it/s]4.04it/
+ 34/49 16G 0.014 0.01113 0 93 1280: 95%|█████████▌| 60/63 [01:47<00:07, 2.45s/it]4.04it/
+ 34/49 16G 0.014 0.01117 0 108 1280: 97%|█████████▋| 61/63 [01:47<00:03, 1.77s/it]4.04it/
+ 34/49 16G 0.01403 0.01122 0 73 1280: 100%|██████████| 63/63 [01:50<00:00, 1.75s/it]4.04it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.06it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.06it/
+ 35/49 16G 0.0141 0.01203 0 87 1280: 17%|█▋ | 11/63 [00:11<00:26, 1.96it/s]4.06it/
+ 35/49 16G 0.01411 0.0119 0 111 1280: 21%|██ | 13/63 [00:21<01:59, 2.38s/it]4.06it/
+ 35/49 16G 0.01418 0.01206 0 109 1280: 22%|██■| 14/63 [00:21<01:24, 1.72s/it]4.06it/
+ 35/49 16G 0.01413 0.01142 0 77 1280: 30%|███ | 19/63 [00:24<00:25, 1.70it/s]4.06it/
+ 35/49 16G 0.01413 0.01165 0 103 1280: 33%|███▎ | 21/63 [00:41<02:41, 3.85s/it]4.06it/
+ 35/49 16G 0.0141 0.0115 0 69 1280: 43%|████▎ | 27/63 [00:43<00:24, 1.47it/s]4.06it/
+ 35/49 16G 0.01395 0.01136 0 85 1280: 54%|█████■| 34/63 [00:57<00:20, 1.39it/s]4.06it/
+ 35/49 16G 0.01394 0.01137 0 93 1280: 56%|█████▌ | 35/63 [00:57<00:15, 1.79it/s]4.06it/
+ 35/49 16G 0.01394 0.01124 0 64 1280: 60%|██████ | 38/63 [01:08<00:45, 1.83s/it]4.06it/
+ 35/49 16G 0.0139 0.01114 0 70 1280: 68%|██████▊ | 43/63 [01:10<00:11, 1.74it/s]4.06it/
+ 35/49 16G 0.01388 0.01114 0 93 1280: 71%|███████■| 45/63 [01:21<00:45, 2.51s/it]4.06it/
+ 35/49 16G 0.01385 0.01106 0 56 1280: 73%|███████▎ | 46/63 [01:21<00:30, 1.81s/it]4.06it/
+ 35/49 16G 0.01378 0.01109 0 82 1280: 81%|████████ | 51/63 [01:24<00:06, 1.72it/s]4.06it/
+ 35/49 16G 0.01378 0.01106 0 71 1280: 86%|████████▌ | 54/63 [01:34<00:16, 1.79s/it]4.06it/
+ 35/49 16G 0.01376 0.0111 0 87 1280: 90%|█████████ | 57/63 [01:37<00:06, 1.02s/it]4.06it/
+ 35/49 16G 0.01373 0.01113 0 86 1280: 94%|█████████▎| 59/63 [01:37<00:02, 1.69it/s]4.06it/
+ 35/49 16G 0.01377 0.01112 0 49 1280: 100%|██████████| 63/63 [01:48<00:00, 1.72s/it]4.06it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.06it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.06it/
+ 36/49 16G 0.0132 0.009729 0 71 1280: 19%|█▉ | 12/63 [00:11<00:20, 2.43it/s]4.06it/
+ 36/49 16G 0.01293 0.01022 0 112 1280: 32%|███■| 20/63 [00:24<00:20, 2.07it/s]4.06it/
+ 36/49 16G 0.01292 0.01028 0 103 1280: 43%|████▎ | 27/63 [00:42<00:30, 1.20it/s]4.06it/
+ 36/49 16G 0.01298 0.01042 0 114 1280: 44%|████■| 28/63 [00:42<00:23, 1.47it/s]4.06it/
+ 36/49 16G 0.01289 0.01041 0 76 1280: 49%|████▉ | 31/63 [00:56<01:14, 2.33s/it]4.06it/
+ 36/49 16G 0.01291 0.01059 0 78 1280: 57%|█████▋ | 36/63 [00:58<00:15, 1.75it/s]4.06it/
+ 36/49 16G 0.0129 0.01065 0 97 1280: 70%|██████▉ | 44/63 [01:11<00:09, 1.93it/s]4.06it/
+ 36/49 16G 0.0129 0.01055 0 73 1280: 76%|███████▌ | 48/63 [01:22<00:21, 1.41s/it]4.06it/
+ 36/49 16G 0.01295 0.01051 0 123 1280: 83%|████████▎ | 52/63 [01:24<00:06, 1.61it/s]4.06it/
+ 36/49 16G 0.01299 0.01041 0 90 1280: 92%|█████████â–| 58/63 [01:36<00:04, 1.24it/s]4.06it/
+ 36/49 16G 0.01298 0.01045 0 120 1280: 95%|█████████▌| 60/63 [01:37<00:01, 1.61it/s]4.06it/
+ 36/49 16G 0.013 0.01048 0 90 1280: 98%|█████████▊| 62/63 [01:49<00:02, 2.72s/it]4.06it/
+ 36/49 16G 0.01303 0.01053 0 67 1280: 100%|██████████| 63/63 [01:49<00:00, 1.73s/it]4.06it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.98it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.98it/
+ 37/49 16G 0.01241 0.009625 0 82 1280: 21%|██ | 13/63 [00:12<00:24, 2.01it/s]3.98it/
+ 37/49 16G 0.01248 0.00997 0 83 1280: 33%|███▎ | 21/63 [00:25<00:24, 1.70it/s]3.98it/
+ 37/49 16G 0.01247 0.009954 0 77 1280: 37%|███▋ | 23/63 [00:43<02:45, 4.14s/it]3.98it/
+ 37/49 16G 0.01244 0.01002 0 98 1280: 46%|████▌ | 29/63 [00:45<00:26, 1.30it/s]3.98it/
+ 37/49 16G 0.01245 0.01009 0 107 1280: 49%|████▉ | 31/63 [00:57<01:31, 2.85s/it]3.98it/
+ 37/49 16G 0.01271 0.01039 0 99 1280: 59%|█████▊ | 37/63 [00:59<00:13, 1.94it/s]3.98it/
+ 37/49 16G 0.01267 0.0105 0 106 1280: 70%|██████▉ | 44/63 [01:12<00:11, 1.60it/s]3.98it/
+ 37/49 16G 0.01266 0.01047 0 83 1280: 71%|███████■| 45/63 [01:12<00:08, 2.03it/s]3.98it/
+ 37/49 16G 0.01268 0.01044 0 74 1280: 76%|███████▌ | 48/63 [01:23<00:29, 1.94s/it]3.98it/
+ 37/49 16G 0.01257 0.01032 0 59 1280: 84%|████████■| 53/63 [01:25<00:05, 1.91it/s]3.98it/
+ 37/49 16G 0.01262 0.01044 0 124 1280: 95%|█████████▌| 60/63 [01:37<00:01, 1.60it/s]3.98it/
+ 37/49 16G 0.01265 0.01041 0 74 1280: 97%|█████████▋| 61/63 [01:38<00:01, 1.86it/s]3.98it/
+ 37/49 16G 0.01263 0.01044 0 106 1280: 98%|█████████▊| 62/63 [01:50<00:03, 3.90s/it]3.98it/
+ 37/49 16G 0.01262 0.01046 0 58 1280: 100%|██████████| 63/63 [01:50<00:00, 1.75s/it]3.98it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.97it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.97it/
+ 38/49 16G 0.01198 0.01111 0 89 1280: 17%|█▋ | 11/63 [00:10<00:42, 1.21it/s]3.97it/
+ 38/49 16G 0.01243 0.01121 0 70 1280: 22%|██■| 14/63 [00:11<00:21, 2.23it/s]3.97it/
+ 38/49 16G 0.01251 0.01123 0 78 1280: 35%|███■| 22/63 [00:24<00:22, 1.84it/s]3.97it/
+ 38/49 16G 0.01235 0.01083 0 75 1280: 41%|████■| 26/63 [00:36<00:53, 1.45s/it]3.97it/
+ 38/49 16G 0.01233 0.01077 0 83 1280: 48%|████▊ | 30/63 [00:37<00:17, 1.86it/s]3.97it/
+ 38/49 16G 0.01235 0.01081 0 113 1280: 54%|█████■| 34/63 [00:58<01:08, 2.36s/it]3.97it/
+ 38/49 16G 0.01238 0.01073 0 91 1280: 60%|██████ | 38/63 [00:59<00:17, 1.42it/s]3.97it/
+ 38/49 16G 0.01241 0.01088 0 110 1280: 73%|███████▎ | 46/63 [01:12<00:08, 1.98it/s]3.97it/
+ 38/49 16G 0.01234 0.01084 0 68 1280: 83%|████████▎ | 52/63 [01:25<00:08, 1.28it/s]3.97it/
+ 38/49 16G 0.01232 0.0109 0 104 1280: 86%|████████▌ | 54/63 [01:25<00:04, 2.11it/s]3.97it/
+ 38/49 16G 0.01232 0.01084 0 116 1280: 98%|█████████▊| 62/63 [01:38<00:00, 2.03it/s]3.97it/
+ 38/49 16G 0.01232 0.01084 0 52 1280: 100%|██████████| 63/63 [01:44<00:00, 1.66s/it]3.97it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.97it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.97it/
+ 39/49 16G 0.01186 0.0101 0 81 1280: 11%|â–ˆ | 7/63 [00:03<00:28, 1.96it/s] 3.97it/
+ 39/49 16G 0.01212 0.01022 0 85 1280: 16%|█▌ | 10/63 [00:10<01:12, 1.36s/it]3.97it/
+ 39/49 16G 0.01216 0.009851 0 73 1280: 19%|█▉ | 12/63 [00:12<00:50, 1.01it/s]3.97it/
+ 39/49 16G 0.01219 0.01013 0 83 1280: 24%|██■| 15/63 [00:16<00:48, 1.00s/it]3.97it/
+ 39/49 16G 0.01211 0.0103 0 109 1280: 32%|███■| 20/63 [00:25<00:45, 1.05s/it]3.97it/
+ 39/49 16G 0.01218 0.01032 0 98 1280: 35%|███■| 22/63 [00:29<00:53, 1.32s/it]3.97it/
+ 39/49 16G 0.01213 0.01044 0 106 1280: 37%|███▋ | 23/63 [00:29<00:41, 1.05s/it]3.97it/
+ 39/49 16G 0.0121 0.01045 0 106 1280: 41%|████■| 26/63 [00:37<00:56, 1.52s/it]3.97it/
+ 39/49 16G 0.01203 0.01036 0 80 1280: 44%|████■| 28/63 [00:38<00:36, 1.05s/it]3.97it/
+ 39/49 16G 0.01196 0.01021 0 72 1280: 49%|████▉ | 31/63 [00:42<00:33, 1.05s/it]3.97it/
+ 39/49 16G 0.01195 0.01014 0 94 1280: 56%|█████▌ | 35/63 [00:56<00:53, 1.91s/it]3.97it/
+ 39/49 16G 0.0119 0.01005 0 71 1280: 60%|██████ | 38/63 [01:00<00:30, 1.23s/it]3.97it/
+ 39/49 16G 0.0119 0.01021 0 155 1280: 62%|██████■| 39/63 [01:01<00:28, 1.18s/it]3.97it/
+ 39/49 16G 0.01197 0.01023 0 102 1280: 70%|██████▉ | 44/63 [01:11<00:19, 1.02s/it]3.97it/
+ 39/49 16G 0.01192 0.01022 0 63 1280: 75%|███████■| 47/63 [01:16<00:19, 1.22s/it]3.97it/
+ 39/49 16G 0.01189 0.01018 0 86 1280: 83%|████████▎ | 52/63 [01:24<00:09, 1.14it/s]3.97it/
+ 39/49 16G 0.01186 0.01008 0 60 1280: 87%|████████▋ | 55/63 [01:28<00:07, 1.02it/s]3.97it/
+ 39/49 16G 0.01187 0.01018 0 113 1280: 92%|█████████â–| 58/63 [01:37<00:08, 1.63s/it]3.97it/
+ 39/49 16G 0.01186 0.0102 0 100 1280: 95%|█████████▌| 60/63 [01:37<00:02, 1.10it/s]3.97it/
+ 39/49 16G 0.01185 0.01019 0 64 1280: 100%|██████████| 63/63 [01:42<00:00, 1.62s/it]3.97it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ 40/49 16G 0.01111 0.01028 0 87 1280: 8%|â–Š | 5/63 [00:07<00:43, 1.33it/s] 4.03it/
+ 40/49 16G 0.01179 0.01029 0 68 1280: 11%|â–ˆ | 7/63 [00:11<01:07, 1.21s/it] 4.03it/
+ 40/49 16G 0.01182 0.01007 0 74 1280: 13%|█▎ | 8/63 [00:11<00:48, 1.13it/s] 4.03it/
+ 40/49 16G 0.01133 0.01033 0 91 1280: 21%|██ | 13/63 [00:20<00:45, 1.11it/s]4.03it/
+ 40/49 16G 0.01146 0.01019 0 62 1280: 25%|██▌ | 16/63 [00:24<00:44, 1.06it/s]4.03it/
+ 40/49 16G 0.01149 0.009993 0 73 1280: 30%|███ | 19/63 [00:33<01:11, 1.64s/it]4.03it/
+ 40/49 16G 0.0114 0.009705 0 67 1280: 33%|███▎ | 21/63 [00:33<00:39, 1.06it/s]4.03it/
+ 40/49 16G 0.0115 0.009961 0 86 1280: 38%|███▊ | 24/63 [00:37<00:36, 1.06it/s]4.03it/
+ 40/49 16G 0.01165 0.00993 0 94 1280: 46%|████▌ | 29/63 [00:46<00:31, 1.06it/s]4.03it/
+ 40/49 16G 0.01157 0.009958 0 84 1280: 51%|█████ | 32/63 [00:50<00:29, 1.05it/s]4.03it/
+ 40/49 16G 0.01159 0.01005 0 115 1280: 56%|█████▌ | 35/63 [01:04<01:09, 2.50s/it]4.03it/
+ 40/49 16G 0.01168 0.01022 0 98 1280: 59%|█████▊ | 37/63 [01:06<00:45, 1.74s/it]4.03it/
+ 40/49 16G 0.0116 0.01014 0 108 1280: 63%|██████▎ | 40/63 [01:08<00:22, 1.02it/s]4.03it/
+ 40/49 16G 0.01166 0.01019 0 113 1280: 71%|███████■| 45/63 [01:21<00:20, 1.14s/it]4.03it/
+ 40/49 16G 0.01166 0.01012 0 92 1280: 75%|███████■| 47/63 [01:23<00:16, 1.03s/it]4.03it/
+ 40/49 16G 0.01163 0.01015 0 113 1280: 76%|███████▌ | 48/63 [01:24<00:15, 1.01s/it]4.03it/
+ 40/49 16G 0.01157 0.01007 0 72 1280: 79%|███████▉ | 50/63 [01:33<00:31, 2.44s/it]4.03it/
+ 40/49 16G 0.01156 0.009983 0 79 1280: 84%|████████■| 53/63 [01:34<00:09, 1.04it/s]4.03it/
+ 40/49 16G 0.0116 0.01015 0 119 1280: 89%|████████▉ | 56/63 [01:38<00:06, 1.04it/s]4.03it/
+ 40/49 16G 0.01156 0.01008 0 101 1280: 97%|█████████▋| 61/63 [01:47<00:02, 1.00s/it]4.03it/
+ 40/49 16G 0.01156 0.01016 0 71 1280: 100%|██████████| 63/63 [01:51<00:00, 1.77s/it]4.03it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.04it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.04it/
+ 41/49 16G 0.01018 0.009997 0 100 1280: 5%|â– | 3/63 [00:05<01:51, 1.86s/it] 4.04it/
+ 41/49 16G 0.01009 0.01013 0 81 1280: 10%|â–‰ | 6/63 [00:06<00:42, 1.34it/s] 4.04it/
+ 41/49 16G 0.01034 0.009715 0 82 1280: 14%|█■| 9/63 [00:11<00:52, 1.03it/s] 4.04it/
+ 41/49 16G 0.01042 0.01008 0 97 1280: 21%|██ | 13/63 [00:19<00:56, 1.12s/it]4.04it/
+ 41/49 16G 0.01046 0.01002 0 86 1280: 22%|██■| 14/63 [00:19<00:47, 1.03it/s]4.04it/
+ 41/49 16G 0.01063 0.01035 0 131 1280: 27%|██▋ | 17/63 [00:24<00:48, 1.06s/it]4.04it/
+ 41/49 16G 0.01062 0.01014 0 89 1280: 35%|███■| 22/63 [00:33<00:40, 1.02it/s]4.04it/
+ 41/49 16G 0.01074 0.01035 0 79 1280: 40%|███▉ | 25/63 [00:37<00:40, 1.07s/it]4.04it/
+ 41/49 16G 0.01068 0.01011 0 91 1280: 46%|████▌ | 29/63 [00:45<00:38, 1.12s/it]4.04it/
+ 41/49 16G 0.01065 0.01014 0 99 1280: 48%|████▊ | 30/63 [00:46<00:31, 1.03it/s]4.04it/
+ 41/49 16G 0.01072 0.01023 0 102 1280: 52%|█████■| 33/63 [00:50<00:31, 1.06s/it]4.04it/
+ 41/49 16G 0.01065 0.01017 0 91 1280: 60%|██████ | 38/63 [00:59<00:24, 1.03it/s]4.04it/
+ 41/49 16G 0.01065 0.01024 0 110 1280: 65%|██████▌ | 41/63 [01:11<00:47, 2.14s/it]4.04it/
+ 41/49 16G 0.01071 0.01023 0 76 1280: 73%|███████▎ | 46/63 [01:21<00:22, 1.30s/it]4.04it/
+ 41/49 16G 0.01073 0.01017 0 99 1280: 78%|███████▊ | 49/63 [01:25<00:14, 1.04s/it]4.04it/
+ 41/49 16G 0.01071 0.01002 0 73 1280: 86%|████████▌ | 54/63 [01:33<00:07, 1.17it/s]4.04it/
+ 41/49 16G 0.01071 0.009971 0 89 1280: 90%|█████████ | 57/63 [01:38<00:06, 1.10s/it]4.04it/
+ 41/49 16G 0.01073 0.01004 0 91 1280: 95%|█████████▌| 60/63 [01:46<00:04, 1.48s/it]4.04it/
+ 41/49 16G 0.01073 0.009994 0 35 1280: 100%|██████████| 63/63 [01:46<00:00, 1.69s/it]4.04it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.03it/
+ 42/49 16G 0.01068 0.009499 0 84 1280: 16%|█▌ | 10/63 [00:11<00:22, 2.36it/s]4.03it/
+ 42/49 16G 0.01076 0.009855 0 89 1280: 29%|██▊ | 18/63 [00:24<00:21, 2.05it/s]4.03it/
+ 42/49 16G 0.01067 0.009801 0 86 1280: 35%|███■| 22/63 [00:36<00:59, 1.46s/it]4.03it/
+ 42/49 16G 0.01051 0.009763 0 113 1280: 41%|████■| 26/63 [00:37<00:18, 2.04it/s]4.03it/
+ 42/49 16G 0.01048 0.009675 0 109 1280: 54%|█████■| 34/63 [00:50<00:14, 2.05it/s]4.03it/
+ 42/49 16G 0.01045 0.009621 0 81 1280: 57%|█████▋ | 36/63 [01:02<01:14, 2.78s/it]4.03it/
+ 42/49 16G 0.0104 0.00961 0 96 1280: 67%|██████▋ | 42/63 [01:03<00:10, 2.05it/s]4.03it/
+ 42/49 16G 0.01034 0.009487 0 83 1280: 76%|███████▌ | 48/63 [01:23<00:17, 1.14s/it]4.03it/
+ 42/49 16G 0.01035 0.00952 0 108 1280: 79%|███████▉ | 50/63 [01:23<00:09, 1.38it/s]4.03it/
+ 42/49 16G 0.01034 0.009515 0 81 1280: 81%|████████ | 51/63 [01:39<01:01, 5.13s/it]4.03it/
+ 42/49 16G 0.01034 0.00947 0 106 1280: 92%|█████████â–| 58/63 [01:40<00:03, 1.60it/s]4.03it/
+ 42/49 16G 0.01034 0.009524 0 94 1280: 98%|█████████▊| 62/63 [01:51<00:01, 1.27s/it]4.03it/
+ 42/49 16G 0.01035 0.009521 0 46 1280: 100%|██████████| 63/63 [01:51<00:00, 1.77s/it]4.03it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+ 43/49 16G 0.009967 0.009406 0 99 1280: 17%|█▋ | 11/63 [00:11<00:25, 2.00it/s]4.02it/
+ 43/49 16G 0.009777 0.009233 0 77 1280: 30%|███ | 19/63 [00:24<00:24, 1.82it/s]4.02it/
+ 43/49 16G 0.009833 0.00892 0 68 1280: 38%|███▊ | 24/63 [00:36<00:40, 1.05s/it]4.02it/
+ 43/49 16G 0.009942 0.009076 0 104 1280: 43%|████▎ | 27/63 [00:37<00:20, 1.79it/s]4.02it/
+ 43/49 16G 0.009853 0.009029 0 82 1280: 54%|█████■| 34/63 [00:50<00:21, 1.38it/s]4.02it/
+ 43/49 16G 0.009853 0.009143 0 129 1280: 56%|█████▌ | 35/63 [00:51<00:15, 1.78it/s]4.02it/
+ 43/49 16G 0.009933 0.009228 0 93 1280: 62%|██████■| 39/63 [01:02<00:33, 1.41s/it]4.02it/
+ 43/49 16G 0.01002 0.009329 0 99 1280: 68%|██████▊ | 43/63 [01:04<00:11, 1.77it/s]4.02it/
+ 43/49 16G 0.01002 0.00932 0 92 1280: 70%|██████▉ | 44/63 [01:20<01:43, 5.42s/it]4.02it/
+ 43/49 16G 0.01004 0.00937 0 110 1280: 71%|███████■| 45/63 [01:22<01:17, 4.30s/it]4.02it/
+ 43/49 16G 0.01003 0.009423 0 102 1280: 76%|███████▌ | 48/63 [01:24<00:28, 1.90s/it]4.02it/
+ 43/49 16G 0.01006 0.009449 0 95 1280: 78%|███████▊ | 49/63 [01:26<00:24, 1.74s/it]4.02it/
+ 43/49 16G 0.01012 0.009554 0 120 1280: 81%|████████ | 51/63 [01:29<00:18, 1.56s/it]4.02it/
+ 43/49 16G 0.01012 0.009593 0 92 1280: 94%|█████████▎| 59/63 [01:37<00:01, 2.29it/s]4.02it/
+ 43/49 16G 0.01014 0.009587 0 88 1280: 97%|█████████▋| 61/63 [01:49<00:05, 2.79s/it]4.02it/
+ 43/49 16G 0.01014 0.009579 0 57 1280: 100%|██████████| 63/63 [01:50<00:00, 1.75s/it]4.02it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.98it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.98it/
+ 44/49 16G 0.01018 0.008905 0 67 1280: 16%|█▌ | 10/63 [00:10<00:38, 1.37it/s]3.98it/
+ 44/49 16G 0.00997 0.008718 0 74 1280: 19%|█▉ | 12/63 [00:11<00:22, 2.25it/s]3.98it/
+ 44/49 16G 0.00991 0.00883 0 108 1280: 21%|██ | 13/63 [00:22<03:12, 3.84s/it]3.98it/
+ 44/49 16G 0.009629 0.008722 0 102 1280: 32%|███■| 20/63 [00:24<00:22, 1.89it/s]3.98it/
+ 44/49 16G 0.009576 0.008837 0 100 1280: 40%|███▉ | 25/63 [00:36<00:40, 1.07s/it]3.98it/
+ 44/49 16G 0.009545 0.008856 0 104 1280: 44%|████■| 28/63 [00:37<00:18, 1.88it/s]3.98it/
+ 44/49 16G 0.009407 0.00902 0 93 1280: 57%|█████▋ | 36/63 [00:50<00:14, 1.87it/s]3.98it/
+ 44/49 16G 0.009443 0.008943 0 72 1280: 63%|██████▎ | 40/63 [01:02<00:33, 1.44s/it]3.98it/
+ 44/49 16G 0.009412 0.008976 0 68 1280: 70%|██████▉ | 44/63 [01:04<00:10, 1.81it/s]3.98it/
+ 44/49 16G 0.009457 0.009065 0 104 1280: 78%|███████▊ | 49/63 [01:16<00:15, 1.08s/it]3.98it/
+ 44/49 16G 0.009478 0.009092 0 87 1280: 83%|████████▎ | 52/63 [01:17<00:06, 1.62it/s]3.98it/
+ 44/49 16G 0.009499 0.009016 0 81 1280: 95%|█████████▌| 60/63 [01:37<00:02, 1.48it/s]3.98it/
+ 44/49 16G 0.00951 0.008997 0 52 1280: 100%|██████████| 63/63 [01:48<00:00, 1.73s/it]3.98it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.99it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.99it/
+ 45/49 16G 0.009296 0.00869 0 108 1280: 13%|█▎ | 8/63 [00:10<01:23, 1.52s/it] 3.99it/
+ 45/49 16G 0.009327 0.009047 0 88 1280: 21%|██ | 13/63 [00:11<00:24, 2.01it/s]3.99it/
+ 45/49 16G 0.00955 0.00916 0 112 1280: 29%|██▊ | 18/63 [00:23<00:46, 1.04s/it]3.99it/
+ 45/49 16G 0.009557 0.008976 0 82 1280: 33%|███▎ | 21/63 [00:25<00:24, 1.70it/s]3.99it/
+ 45/49 16G 0.009532 0.008948 0 77 1280: 35%|███■| 22/63 [00:36<02:34, 3.77s/it]3.99it/
+ 45/49 16G 0.009589 0.009163 0 119 1280: 46%|████▌ | 29/63 [00:38<00:19, 1.72it/s]3.99it/
+ 45/49 16G 0.009598 0.009088 0 111 1280: 54%|█████■| 34/63 [00:50<00:30, 1.05s/it]3.99it/
+ 45/49 16G 0.009656 0.009066 0 92 1280: 59%|█████▊ | 37/63 [00:51<00:14, 1.74it/s]3.99it/
+ 45/49 16G 0.009637 0.009132 0 118 1280: 68%|██████▊ | 43/63 [01:04<00:19, 1.00it/s]3.99it/
+ 45/49 16G 0.00962 0.009139 0 72 1280: 71%|███████■| 45/63 [01:04<00:10, 1.72it/s]3.99it/
+ 45/49 16G 0.009616 0.009146 0 77 1280: 78%|███████▊ | 49/63 [01:16<00:19, 1.40s/it]3.99it/
+ 45/49 16G 0.009609 0.009239 0 99 1280: 84%|████████■| 53/63 [01:17<00:05, 1.72it/s]3.99it/
+ 45/49 16G 0.009621 0.009231 0 90 1280: 89%|████████▉ | 56/63 [01:36<00:21, 3.01s/it]3.99it/
+ 45/49 16G 0.009661 0.009217 0 103 1280: 97%|█████████▋| 61/63 [01:37<00:01, 1.48it/s]3.99it/
+ 45/49 16G 0.009659 0.009199 0 75 1280: 100%|██████████| 63/63 [01:49<00:00, 1.74s/it]3.99it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.89it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.89it/
+ 46/49 16G 0.008917 0.008715 0 92 1280: 14%|█■| 9/63 [00:09<01:16, 1.41s/it] 3.89it/
+ 46/49 16G 0.008807 0.008587 0 72 1280: 22%|██■| 14/63 [00:11<00:22, 2.20it/s]3.89it/
+ 46/49 16G 0.008806 0.008886 0 107 1280: 30%|███ | 19/63 [00:23<00:46, 1.05s/it]3.89it/
+ 46/49 16G 0.008912 0.0089 0 95 1280: 35%|███■| 22/63 [00:24<00:22, 1.83it/s]3.89it/
+ 46/49 16G 0.008934 0.008889 0 59 1280: 48%|████▊ | 30/63 [00:37<00:18, 1.83it/s]3.89it/
+ 46/49 16G 0.009019 0.008776 0 67 1280: 54%|█████■| 34/63 [00:49<00:41, 1.43s/it]3.89it/
+ 46/49 16G 0.009122 0.008688 0 93 1280: 60%|██████ | 38/63 [00:50<00:13, 1.86it/s]3.89it/
+ 46/49 16G 0.009192 0.008616 0 89 1280: 71%|███████■| 45/63 [01:03<00:12, 1.44it/s]3.89it/
+ 46/49 16G 0.009166 0.008573 0 68 1280: 73%|███████▎ | 46/63 [01:04<00:09, 1.85it/s]3.89it/
+ 46/49 16G 0.009143 0.008477 0 91 1280: 78%|███████▊ | 49/63 [01:15<00:27, 1.95s/it]3.89it/
+ 46/49 16G 0.009129 0.008505 0 74 1280: 86%|████████▌ | 54/63 [01:17<00:04, 1.82it/s]3.89it/
+ 46/49 16G 0.009136 0.008648 0 87 1280: 95%|█████████▌| 60/63 [01:36<00:03, 1.19s/it]3.89it/
+ 46/49 16G 0.009152 0.008728 0 93 1280: 98%|█████████▊| 62/63 [01:36<00:00, 1.44it/s]3.89it/
+ 46/49 16G 0.009163 0.008728 0 52 1280: 100%|██████████| 63/63 [01:43<00:00, 1.64s/it]3.89it/
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:02<00:00, 3.43it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.71it/
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 2.71it/
+ 47/49 16G 0.008766 0.008579 0 74 1280: 11%|â–ˆ | 7/63 [00:03<00:17, 3.15it/s] 2.71it/
+ 47/49 16G 0.008997 0.00888 0 119 1280: 16%|█▌ | 10/63 [00:09<00:58, 1.10s/it]2.71it/
+ 47/49 16G 0.00881 0.008917 0 120 1280: 19%|█▉ | 12/63 [00:11<00:46, 1.10it/s]2.71it/
+ 47/49 16G 0.008871 0.008901 0 91 1280: 24%|██■| 15/63 [00:16<00:52, 1.09s/it]2.71it/
+ 47/49 16G 0.008922 0.00886 0 95 1280: 29%|██▊ | 18/63 [00:22<01:01, 1.37s/it]2.71it/
+ 47/49 16G 0.008816 0.008924 0 124 1280: 32%|███■| 20/63 [00:24<00:48, 1.13s/it]2.71it/
+ 47/49 16G 0.008682 0.008749 0 64 1280: 37%|███▋ | 23/63 [00:29<00:45, 1.14s/it]2.71it/
+ 47/49 16G 0.008758 0.00877 0 81 1280: 41%|████■| 26/63 [00:35<00:51, 1.39s/it]2.71it/
+ 47/49 16G 0.008784 0.008803 0 96 1280: 43%|████▎ | 27/63 [00:37<00:54, 1.52s/it]2.71it/
+ 47/49 16G 0.008717 0.008758 0 87 1280: 44%|████■| 28/63 [00:37<00:39, 1.12s/it]2.71it/
+ 47/49 16G 0.008714 0.008808 0 83 1280: 49%|████▉ | 31/63 [00:42<00:36, 1.14s/it]2.71it/
+ 47/49 16G 0.008709 0.008896 0 113 1280: 54%|█████■| 34/63 [00:49<00:39, 1.38s/it]2.71it/
+ 47/49 16G 0.00868 0.008902 0 92 1280: 57%|█████▋ | 36/63 [00:51<00:30, 1.13s/it]2.71it/
+ 47/49 16G 0.008683 0.008857 0 83 1280: 60%|██████ | 38/63 [00:55<00:38, 1.55s/it]2.71it/
+ 47/49 16G 0.008696 0.00886 0 68 1280: 62%|██████■| 39/63 [00:55<00:27, 1.14s/it]2.71it/
+ 47/49 16G 0.008726 0.008906 0 83 1280: 65%|██████▌ | 41/63 [01:01<00:39, 1.80s/it]2.71it/
+ 47/49 16G 0.008733 0.008888 0 93 1280: 67%|██████▋ | 42/63 [01:02<00:29, 1.40s/it]2.71it/
+ 47/49 16G 0.00871 0.008905 0 103 1280: 70%|██████▉ | 44/63 [01:04<00:21, 1.12s/it]2.71it/
+ 47/49 16G 0.008759 0.008933 0 134 1280: 75%|███████■| 47/63 [01:09<00:18, 1.13s/it]2.71it/
+ 47/49 16G 0.008758 0.00892 0 110 1280: 79%|███████▉ | 50/63 [01:15<00:17, 1.36s/it]2.71it/
+ 47/49 16G 0.008769 0.00891 0 64 1280: 83%|████████▎ | 52/63 [01:17<00:12, 1.10s/it]2.71it/
+ 47/49 16G 0.008796 0.008988 0 127 1280: 86%|████████▌ | 54/63 [01:21<00:13, 1.53s/it]2.71it/
+ 47/49 16G 0.008791 0.008963 0 84 1280: 87%|████████▋ | 55/63 [01:22<00:08, 1.12s/it]2.71it/
+ 47/49 16G 0.008758 0.008983 0 100 1280: 92%|█████████â–| 58/63 [01:28<00:06, 1.36s/it]2.71it/
+ 47/49 16G 0.008732 0.008925 0 104 1280: 95%|█████████▌| 60/63 [01:30<00:03, 1.09s/it]2.71it/
+ 47/49 16G 0.008753 0.008941 0 43 1280: 100%|██████████| 63/63 [01:41<00:00, 1.61s/it]2.71it/
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.30it/
+ 48/49 16G 0.009268 0.01285 0 124 1280: 2%|â– | 1/63 [00:02<02:54, 2.81s/it] 3.30it/
+ 48/49 16G 0.009042 0.008958 0 114 1280: 8%|â–Š | 5/63 [00:04<00:32, 1.77it/s] 3.30it/
+ 48/49 16G 0.009007 0.008807 0 83 1280: 10%|â–‰ | 6/63 [00:07<01:10, 1.24s/it] 3.30it/
+ 48/49 16G 0.008751 0.00845 0 82 1280: 13%|█▎ | 8/63 [00:07<00:44, 1.23it/s] 3.30it/
+ 48/49 16G 0.008765 0.008923 0 110 1280: 17%|█▋ | 11/63 [00:16<01:24, 1.63s/it]3.30it/
+ 48/49 16G 0.008849 0.0091 0 97 1280: 21%|██ | 13/63 [00:18<00:54, 1.08s/it]3.30it/
+ 48/49 16G 0.008685 0.008582 0 62 1280: 25%|██▌ | 16/63 [00:22<00:50, 1.08s/it]3.30it/
+ 48/49 16G 0.00865 0.008609 0 85 1280: 27%|██▋ | 17/63 [00:29<02:03, 2.69s/it]3.30it/
+ 48/49 16G 0.008728 0.008548 0 83 1280: 30%|███ | 19/63 [00:29<01:06, 1.51s/it]3.30it/
+ 48/49 16G 0.008759 0.008546 0 70 1280: 33%|███▎ | 21/63 [00:31<00:47, 1.14s/it]3.30it/
+ 48/49 16G 0.008812 0.008725 0 128 1280: 38%|███▊ | 24/63 [00:36<00:42, 1.10s/it]3.30it/
+ 48/49 16G 0.008803 0.008829 0 81 1280: 43%|████▎ | 27/63 [00:43<00:54, 1.50s/it]3.30it/
+ 48/49 16G 0.008792 0.008848 0 107 1280: 46%|████▌ | 29/63 [00:44<00:38, 1.12s/it]3.30it/
+ 48/49 16G 0.008746 0.008895 0 87 1280: 49%|████▉ | 31/63 [00:49<00:47, 1.47s/it]3.30it/
+ 48/49 16G 0.008755 0.008911 0 89 1280: 51%|█████ | 32/63 [00:49<00:33, 1.08s/it]3.30it/
+ 48/49 16G 0.008709 0.008786 0 81 1280: 56%|█████▌ | 35/63 [00:56<00:41, 1.49s/it]3.30it/
+ 48/49 16G 0.008666 0.008736 0 73 1280: 59%|█████▊ | 37/63 [00:57<00:28, 1.11s/it]3.30it/
+ 48/49 16G 0.008662 0.008698 0 61 1280: 63%|██████▎ | 40/63 [01:02<00:24, 1.08s/it]3.30it/
+ 48/49 16G 0.008631 0.008582 0 71 1280: 67%|██████▋ | 42/63 [01:08<00:39, 1.89s/it]3.30it/
+ 48/49 16G 0.008675 0.008708 0 104 1280: 71%|███████■| 45/63 [01:11<00:19, 1.11s/it]3.30it/
+ 48/49 16G 0.008595 0.008674 0 64 1280: 76%|███████▌ | 48/63 [01:15<00:16, 1.08s/it]3.30it/
+ 48/49 16G 0.008574 0.008711 0 95 1280: 81%|████████ | 51/63 [01:22<00:17, 1.46s/it]3.30it/
+ 48/49 16G 0.00856 0.00867 0 76 1280: 84%|████████■| 53/63 [01:24<00:11, 1.11s/it]3.30it/
+ 48/49 16G 0.008577 0.00862 0 84 1280: 89%|████████▉ | 56/63 [01:28<00:07, 1.12s/it]3.30it/
+ 48/49 16G 0.008571 0.008605 0 92 1280: 92%|█████████â–| 58/63 [01:35<00:09, 1.86s/it]3.30it/
+ 48/49 16G 0.008591 0.008556 0 95 1280: 95%|█████████▌| 60/63 [01:37<00:04, 1.48s/it]3.30it/
+ 48/49 16G 0.008589 0.008559 0 94 1280: 97%|█████████▋| 61/63 [01:37<00:02, 1.10s/it]3.30it/
+ 48/49 16G 0.008574 0.008543 0 69 1280: 100%|██████████| 63/63 [01:41<00:00, 1.61s/it]3.30it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.95it/
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.95it/
+ 49/49 16G 0.006208 0.006229 0 80 1280: 2%|â– | 1/63 [00:05<05:59, 5.79s/it] 3.95it/
+ 49/49 16G 0.007945 0.007149 0 86 1280: 10%|â–‰ | 6/63 [00:09<00:42, 1.33it/s] 3.95it/
+ 49/49 16G 0.007921 0.007597 0 113 1280: 11%|â–ˆ | 7/63 [00:14<01:49, 1.96s/it] 3.95it/
+ 49/49 16G 0.007948 0.008051 0 103 1280: 14%|█■| 9/63 [00:15<01:06, 1.22s/it] 3.95it/
+ 49/49 16G 0.008085 0.008203 0 88 1280: 16%|█▌ | 10/63 [00:22<02:45, 3.13s/it]3.95it/
+ 49/49 16G 0.00815 0.008469 0 94 1280: 22%|██■| 14/63 [00:23<00:43, 1.13it/s]3.95it/
+ 49/49 16G 0.008123 0.008664 0 89 1280: 27%|██▋ | 17/63 [00:29<00:56, 1.23s/it]3.95it/
+ 49/49 16G 0.008186 0.008515 0 80 1280: 33%|███▎ | 21/63 [00:36<00:58, 1.38s/it]3.95it/
+ 49/49 16G 0.008252 0.008575 0 99 1280: 35%|███■| 22/63 [00:37<00:42, 1.03s/it]3.95it/
+ 49/49 16G 0.0084 0.008744 0 125 1280: 40%|███▉ | 25/63 [00:42<00:45, 1.20s/it]3.95it/
+ 49/49 16G 0.008375 0.008832 0 108 1280: 44%|████■| 28/63 [00:48<00:48, 1.39s/it]3.95it/
+ 49/49 16G 0.008385 0.008788 0 90 1280: 48%|████▊ | 30/63 [00:50<00:34, 1.03s/it]3.95it/
+ 49/49 16G 0.008324 0.008754 0 69 1280: 52%|█████■| 33/63 [00:55<00:35, 1.20s/it]3.95it/
+ 49/49 16G 0.008316 0.00866 0 52 1280: 57%|█████▋ | 36/63 [01:02<00:37, 1.41s/it]3.95it/
+ 49/49 16G 0.008276 0.008634 0 93 1280: 60%|██████ | 38/63 [01:03<00:25, 1.01s/it]3.95it/
+ 49/49 16G 0.008288 0.008557 0 86 1280: 63%|██████▎ | 40/63 [01:08<00:37, 1.62s/it]3.95it/
+ 49/49 16G 0.008302 0.008587 0 110 1280: 65%|██████▌ | 41/63 [01:09<00:26, 1.19s/it]3.95it/
+ 49/49 16G 0.008309 0.008586 0 92 1280: 68%|██████▊ | 43/63 [01:14<00:36, 1.83s/it]3.95it/
+ 49/49 16G 0.008334 0.008639 0 132 1280: 73%|███████▎ | 46/63 [01:16<00:17, 1.00s/it]3.95it/
+ 49/49 16G 0.008312 0.008619 0 89 1280: 78%|███████▊ | 49/63 [01:22<00:16, 1.19s/it]3.95it/
+ 49/49 16G 0.008308 0.008556 0 65 1280: 83%|████████▎ | 52/63 [01:28<00:15, 1.39s/it]3.95it/
+ 49/49 16G 0.008296 0.008535 0 82 1280: 86%|████████▌ | 54/63 [01:29<00:08, 1.01it/s]3.95it/
+ 49/49 16G 0.008323 0.008572 0 96 1280: 90%|█████████ | 57/63 [01:35<00:07, 1.20s/it]3.95it/
+ 49/49 16G 0.008333 0.008566 0 97 1280: 97%|█████████▋| 61/63 [01:42<00:02, 1.33s/it]3.95it/
+ 49/49 16G 0.008354 0.008658 0 83 1280: 100%|██████████| 63/63 [01:43<00:00, 1.64s/it]3.95it/
+50 epochs completed in 1.538 hours.Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+Model summary: 212 layers, 20852934 parameters, 0 gradients, 47.9 GFLOPsmAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+Model summary: 212 layers, 20852934 parameters, 0 gradients, 47.9 GFLOPsmAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+Results saved to [1mruns/train/6beetle_7-non_clean_bkg_overlap[22m, 47.9 GFLOPsmAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
+Results saved to [1mruns/train/6beetle_7-non_clean_bkg_overlap[22m, 47.9 GFLOPsmAP50 mAP50-95: 100%|██████████| 8/8 [00:01<00:00, 4.02it/
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/requirements.txt b/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230305_182425-1r190qb3/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
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\ No newline at end of file
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diff --git a/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/conda-environment.yaml b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/conda-environment.yaml
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--- /dev/null
+++ b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/output.log
@@ -0,0 +1,831 @@
+Overriding model.yaml nc=80 with nc=1
+ from n params module arguments
+ 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
+ 1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
+ 2 -1 2 65280 models.common.C3 [96, 96, 2]
+ 3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
+ 4 -1 4 444672 models.common.C3 [192, 192, 4]
+ 5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
+ 6 -1 6 2512896 models.common.C3 [384, 384, 6]
+ 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
+ 8 -1 2 4134912 models.common.C3 [768, 768, 2]
+ 9 -1 1 1476864 models.common.SPPF [768, 768, 5]
+ 10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
+ 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 12 [-1, 6] 1 0 models.common.Concat [1]
+ 13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
+ 14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
+ 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
+ 16 [-1, 4] 1 0 models.common.Concat [1]
+ 17 -1 2 296448 models.common.C3 [384, 192, 2, False]
+ 18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
+ 19 [-1, 14] 1 0 models.common.Concat [1]
+ 20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
+ 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
+ 22 [-1, 10] 1 0 models.common.Concat [1]
+ 23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
+ 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
+Model summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs
+Transferred 475/481 items from yolov5m.pt
+[34m[1mAMP: [39m[22mchecks passed ✅
+[34m[1moptimizer:[39m[22m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
+[34m[1mtrain: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels... 1001 images, 116 backgrounds, 0 corrupt: 100%|██████████| 1001/1001 [00:00<00:00, 2021.93it/s]
+[34m[1mtrain: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/train/labels.cache
+[34m[1mval: [39m[22mScanning /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels... 249 images, 24 backgrounds, 0 corrupt: 100%|██████████| 249/249 [00:00<00:00, 1073.70it/s]
+[34m[1mval: [39m[22mNew cache created: /projects/akhot2/group-01-phys371-sp2023/crop/data/test/labels.cache
+[34m[1mAutoAnchor: [39m[22m6.33 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
+Plotting labels to runs/train/6beetle_7-non_3dirt_bkg_over/labels.jpg...
+Image sizes 1280 train, 1280 val
+Using 16 dataloader workers
+Logging results to [1mruns/train/6beetle_7-non_3dirt_bkg_over
+Starting training for 50 epochs...
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 0/49 24.7G 0.08205 0.07198 0 38 1280: 100%|██████████| 63/63 [00:41<00:00, 1.52it/s]
+
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:03<00:00, 2.57it/s]
+ all 249 997 0.207 0.529 0.209 0.0588
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
+
+
+
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+
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+ 1/49 28.5G 0.0676 0.04231 0 64 1280: 100%|██████████| 63/63 [00:30<00:00, 2.04it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.69it/s]
+ all 249 997 0.395 0.711 0.458 0.17
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
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+ 2/49 28.5G 0.06533 0.03283 0 53 1280: 100%|██████████| 63/63 [00:32<00:00, 1.92it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.75it/s]
+ all 249 997 0.52 0.826 0.61 0.197
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
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+
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+ 3/49 28.5G 0.0564 0.02834 0 55 1280: 100%|██████████| 63/63 [00:33<00:00, 1.90it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.68it/s]
+ all 249 997 0.68 0.976 0.841 0.412
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
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+
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+
+
+
+ 4/49 28.5G 0.04806 0.02614 0 50 1280: 100%|██████████| 63/63 [00:28<00:00, 2.19it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.84it/s]
+ all 249 997 0.989 0.984 0.985 0.526
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
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+
+
+
+
+
+
+
+
+ 5/49 28.5G 0.04228 0.02292 0 44 1280: 100%|██████████| 63/63 [00:29<00:00, 2.10it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.75it/s]
+ all 249 997 0.989 0.985 0.987 0.537
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
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+ 6/49 28.5G 0.03822 0.02149 0 40 1280: 100%|██████████| 63/63 [00:32<00:00, 1.94it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.69it/s]
+ all 249 997 0.992 0.995 0.994 0.595
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
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+ 7/49 28.5G 0.03598 0.02197 0 36 1280: 100%|██████████| 63/63 [00:31<00:00, 2.02it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.65it/s]
+ all 249 997 0.961 0.99 0.991 0.66
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
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+ 8/49 28.5G 0.03318 0.02073 0 46 1280: 100%|██████████| 63/63 [00:31<00:00, 2.02it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.62it/s]
+ all 249 997 0.998 0.996 0.995 0.662
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
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+ 9/49 28.5G 0.03221 0.02034 0 38 1280: 100%|██████████| 63/63 [00:32<00:00, 1.95it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.68it/s]
+ all 249 997 0.994 0.998 0.995 0.65
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+
+
+
+
+
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+ 10/49 28.5G 0.03058 0.01905 0 57 1280: 100%|██████████| 63/63 [00:31<00:00, 2.00it/s]
+ Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 8/8 [00:02<00:00, 3.76it/s]
+ all 249 997 0.997 0.998 0.995 0.659
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
+ 11/49 28.5G 0.02852 0.0172 0 76 1280: 10%|â–‰ | 6/63 [00:01<00:17, 3.23it/s]
+ 11/49 28.5G 0.02907 0.01755 0 79 1280: 17%|█▋ | 11/63 [00:03<00:15, 3.27it/s]
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+ 11/49 28.5G 0.02938 0.01798 0 110 1280: 68%|██████▊ | 43/63 [00:19<00:06, 3.17it/s]
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+ 11/49 28.5G 0.02921 0.01845 0 101 1280: 90%|█████████ | 57/63 [00:27<00:02, 2.98it/s]
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+ 11/49 28.5G 0.02913 0.01832 0 63 1280: 100%|██████████| 63/63 [00:30<00:00, 2.04it/s]
+ all 249 997 0.999 0.999 0.995 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
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+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.729██████| 63/63 [00:30<00:00, 2.04it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:01, 3.41it/s]
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+ all 249 997 1 0.997 0.995 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.764██████| 63/63 [00:31<00:00, 2.02it/s]3.63it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ all 249 997 0.998 0.999 0.995 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.714: 25%|██▌ | 2/8 [00:00<00:01, 3.23it/s]
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.782: 75%|███████▌ | 6/8 [00:01<00:00, 3.49it/s]
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.801: 75%|███████▌ | 6/8 [00:01<00:00, 3.55it/s]
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.766: 50%|█████ | 4/8 [00:01<00:01, 3.27it/s]
+ Class Images Instances P R mAP50 mAP50-95: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.798: 88%|████████▊ | 7/8 [00:02<00:00, 3.53it/s]
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+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.835: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.837: 62%|██████▎ | 5/8 [00:01<00:00, 3.53it/s]
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+ all 249 997 0.999 0.998 0.995 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.829: 0%| | 0/8 [00:00<?, ?it/s].53it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ all 249 997 1 0.998 0.995 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.857: 12%|█▎ | 1/8 [00:00<00:02, 3.46it/s]
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.85: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ all 249 997 0.999 0.998 0.995 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.871: 12%|█▎ | 1/8 [00:00<00:02, 3.13it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.859: 25%|██▌ | 2/8 [00:00<00:01, 3.38it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ all 249 997 1 0.997 0.995 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.879: 12%|█▎ | 1/8 [00:00<00:02, 3.43it/s]
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 62%|██████▎ | 5/8 [00:01<00:00, 3.32it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ all 249 997 1 0.998 0.995 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.885: 12%|█▎ | 1/8 [00:00<00:02, 3.36it/s]
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.884: 38%|███▊ | 3/8 [00:00<00:01, 3.32it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ all 249 997 1 0.998 0.995 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.881: 25%|██▌ | 2/8 [00:00<00:01, 3.20it/s]
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ all 249 997 1 0.998 0.995 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.892: 50%|█████ | 4/8 [00:01<00:01, 3.50it/s]
+ Class Images Instances P R mAP50 mAP50-95: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.89: 62%|██████▎ | 5/8 [00:01<00:00, 3.57it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ all 249 997 1 0.998 0.995 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.889: 12%|█▎ | 1/8 [00:00<00:02, 3.47it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ all 249 997 1 0.998 0.995 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 12%|█▎ | 1/8 [00:00<00:02, 3.44it/s]
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ all 249 997 1 0.998 0.995 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.909: 50%|█████ | 4/8 [00:01<00:01, 3.46it/s]
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+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.905: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.918: 0%| | 0/8 [00:00<?, ?it/s].46it/s]
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ all 249 997 0.999 0.998 0.995 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.925: 38%|███▊ | 3/8 [00:00<00:01, 3.19it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ all 249 997 1 0.998 0.995 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.908: 25%|██▌ | 2/8 [00:00<00:01, 3.45it/s]
+ Class Images Instances P R mAP50 mAP50-95: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ all 249 997 1 0.998 0.995 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.928: 0%| | 0/8 [00:00<?, ?it/s].45it/s]
+ Class Images Instances P R mAP50 mAP50-95: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.913: 75%|███████▌ | 6/8 [00:01<00:00, 3.61it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ all 249 997 1 0.998 0.995 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.931: 25%|██▌ | 2/8 [00:00<00:01, 3.21it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Class Images Instances P R mAP50 mAP50-95: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 12%|█▎ | 1/8 [00:00<00:02, 3.17it/s]
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ all 249 997 1 0.997 0.995 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.938: 50%|█████ | 4/8 [00:01<00:01, 3.54it/s]
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.936: 50%|█████ | 4/8 [00:01<00:01, 3.55it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ all 249 997 1 0.998 0.995 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.935: 25%|██▌ | 2/8 [00:00<00:02, 2.92it/s]
+ Class Images Instances P R mAP50 mAP50-95: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ all 249 997 1 0.998 0.995 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 50%|█████ | 4/8 [00:01<00:01, 3.62it/s]
+ Class Images Instances P R mAP50 mAP50-95: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ all 249 997 1 0.998 0.995 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0.944: 38%|███▊ | 3/8 [00:00<00:01, 3.53it/s]
+ Class Images Instances P R mAP50 mAP50-95: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+ all 249 997 1 0.998 0.995 0.947: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+Validating runs/train/6beetle_7-non_3dirt_bkg_over/weights/best.pt...ts/best.pt, 42.5MB7: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+Model summary: 212 layers, 20852934 parameters, 0 gradients, 47.9 GFLOPsbest.pt, 42.5MB7: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+Model summary: 212 layers, 20852934 parameters, 0 gradients, 47.9 GFLOPsbest.pt, 42.5MB7: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+ all 249 997 1 0.998 0.995 0.947: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+ all 249 997 1 0.998 0.995 0.947: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+ all 249 997 1 0.998 0.995 0.947: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
+ all 249 997 1 0.998 0.995 0.947: 25%|██▌ | 2/8 [00:00<00:01, 3.44it/s]
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/requirements.txt b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a80c24390ca9a31f38b0630d7799dbc2def0735
--- /dev/null
+++ b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/requirements.txt
@@ -0,0 +1,216 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+astor==0.8.1
+asttokens==2.0.5
+astunparse==1.6.3
+async-timeout==4.0.1
+attrs==21.4.0
+awkward==0.14.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==4.1.0
+blinker==1.4
+bottleneck==1.3.4
+brotlipy==0.7.0
+cachetools==4.2.2
+certifi==2022.12.7
+cffi==1.15.0
+chardet==4.0.0
+charset-normalizer==2.0.4
+click==8.0.4
+cloudpickle==2.0.0
+colorama==0.4.4
+commonmark==0.9.1
+cramjam==2.5.0
+cryptography==3.4.8
+cycler==0.11.0
+cython==0.29.28
+debugpy==1.5.1
+decorator==5.1.1
+defusedxml==0.7.1
+detectron2==0.5
+docker-pycreds==0.4.0
+energyflow==1.3.2
+entrypoints==0.4
+et-xmlfile==1.1.0
+executing==0.8.3
+fastjsonschema==2.15.1
+fastparquet==0.8.1
+filelock==3.7.1
+flatbuffers==1.12
+fonttools==4.25.0
+frozenlist==1.2.0
+fsspec==2022.5.0
+future==0.18.2
+fvcore==0.1.5.post20220506
+gast==0.4.0
+gdown==4.5.1
+gensim==4.1.2
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==2.6.0
+google-pasta==0.2.0
+graphviz==0.20.1
+grpcio==1.42.0
+h5py==3.6.0
+idna==3.4
+imageio==2.19.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+ipykernel==6.9.1
+ipython-genutils==0.2.0
+ipython==8.3.0
+ipywidgets==7.6.5
+jedi==0.18.1
+jinja2==3.0.3
+joblib==1.1.0
+jsonschema==4.4.0
+jupyter-client==7.2.2
+jupyter-console==6.4.3
+jupyter-core==4.10.0
+jupyter==1.0.0
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keras-applications==1.0.8
+keras-preprocessing==1.1.2
+keras==2.9.0
+kiwisolver==1.4.2
+lbn==1.2.2
+libclang==14.0.1
+lime==0.2.0.1
+llvmlite==0.38.0
+markdown==3.3.4
+markupsafe==2.1.1
+matplotlib-inline==0.1.2
+matplotlib==3.5.1
+mistune==0.8.4
+mkl-fft==1.3.1
+mkl-random==1.2.2
+mkl-service==2.4.0
+mock==4.0.3
+modelstore==0.0.74
+multidict==5.2.0
+munkres==1.1.4
+nbclient==0.5.13
+nbconvert==6.4.4
+nbformat==5.3.0
+nest-asyncio==1.5.5
+networkx==2.8.3
+notebook==6.4.11
+numba==0.55.1
+numexpr==2.8.1
+numpy==1.21.5
+oauthlib==3.2.0
+opencv-python==4.7.0.68
+openpyxl==3.0.10
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.2
+pandocfilters==1.5.0
+parso==0.8.3
+pathtools==0.1.2
+pd4ml==0.3
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow-heif==0.9.3
+pillow==9.0.1
+pip==21.2.4
+pluggy==1.0.0
+portalocker==2.3.0
+prometheus-client==0.13.1
+promise==2.3
+prompt-toolkit==3.0.20
+protobuf==3.19.6
+psutil==5.8.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py==1.11.0
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+pycocotools==2.0.2
+pycparser==2.21
+pydot==1.4.2
+pygments==2.11.2
+pyjwt==2.1.0
+pyopenssl==21.0.0
+pyparsing==3.0.9
+pyrsistent==0.18.0
+pysocks==1.7.1
+pytest==7.1.2
+python-dateutil==2.8.2
+python-dotenv==0.21.1
+pytorch-model-summary==0.1.1
+pytz==2022.1
+pywavelets==1.3.0
+pyyaml==6.0
+pyzmq==22.3.0
+qtconsole==5.3.0
+qtpy==2.0.1
+requests-oauthlib==1.3.0
+requests-toolbelt==0.10.1
+requests==2.27.1
+rich==12.4.4
+roboflow==0.2.29
+rsa==4.7.2
+scikit-image==0.19.2
+scikit-learn==1.0.2
+scipy==1.7.3
+seaborn==0.11.2
+send2trash==1.8.0
+sentry-sdk==1.5.12
+setproctitle==1.2.2
+setuptools==67.3.2
+shap==0.40.0
+shortuuid==1.0.8
+sip==4.19.13
+six==1.16.0
+slicer==0.0.7
+smart-open==5.2.1
+smmap==4.0.0
+soupsieve==2.3.1
+stack-data==0.2.0
+tables==3.6.1
+tabulate==0.8.9
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.6.0
+tensorboard==2.9.1
+tensorflow-decision-forests==0.2.6
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+tensorflow==2.9.1
+termcolor==1.1.0
+terminado==0.13.1
+testpath==0.5.0
+thop==0.1.1.post2209072238
+threadpoolctl==2.2.0
+tifffile==2022.5.4
+tomli==2.0.1
+torch==1.11.0
+torchaudio==0.11.0
+torchinfo==1.6.5
+torchvision==0.12.0
+tornado==6.1
+tqdm==4.64.0
+traitlets==5.1.1
+typing-extensions==4.1.1
+uproot-methods==0.9.2
+urllib3==1.26.9
+vector==0.8.5
+wandb==0.12.15
+wasserstein==1.0.1
+wcwidth==0.2.5
+webencodings==0.5.1
+werkzeug==2.0.3
+wget==3.2
+wheel==0.38.4
+widgetsnbextension==3.5.2
+wrapt==1.13.3
+wurlitzer==3.0.2
+xgboost==1.5.1
+yacs==0.1.6
+yarl==1.6.3
+zipp==3.8.0
\ No newline at end of file
diff --git a/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/wandb-metadata.json b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..09ed01720c91d6442474ecab9e56654a7c484900
--- /dev/null
+++ b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/wandb-metadata.json
@@ -0,0 +1,39 @@
+{
+ "os": "Linux-3.10.0-1160.81.1.el7.x86_64-x86_64-with-glibc2.17",
+ "python": "3.9.12",
+ "heartbeatAt": "2023-03-10T15:49:48.103651",
+ "startedAt": "2023-03-10T15:49:44.301850",
+ "docker": null,
+ "gpu": "A100-SXM4-40GB",
+ "gpu_count": 1,
+ "cpu_count": 256,
+ "cuda": "11.0.228",
+ "args": [
+ "--img",
+ "1280",
+ "--batch",
+ "16",
+ "--epochs",
+ "50",
+ "--data",
+ "beetles.yaml",
+ "--weights",
+ "yolov5m.pt",
+ "--workers",
+ "30",
+ "--name",
+ "6beetle_7-non_3dirt_bkg_over"
+ ],
+ "state": "running",
+ "program": "/projects/akhot2/group-01-phys371-sp2023/yolov5_model/train.py",
+ "codePath": "yolov5_model/train.py",
+ "git": {
+ "remote": "https://gitlab.engr.illinois.edu/akhot2/group-01-phys371-sp2023",
+ "commit": "cea9a34de2e8800cc273cc3f2f41176e015fd0a8"
+ },
+ "email": "khotayush@gmail.com",
+ "root": "/projects/akhot2/group-01-phys371-sp2023",
+ "host": "hal-dgx",
+ "username": "akhot2",
+ "executable": "/raid/projects/akhot2/conda/envs/akhot2/bin/python"
+}
diff --git a/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/wandb-summary.json b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..2a3e4635fa55a923571dbcff07c8a2eee6beba6c
--- /dev/null
+++ b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/files/wandb-summary.json
@@ -0,0 +1 @@
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diff --git a/yolov5_model/wandb/run-20230310_094944-c6nn9q90/logs/debug-internal.log b/yolov5_model/wandb/run-20230310_094944-c6nn9q90/logs/debug-internal.log
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