diff --git a/crop/SimData.ipynb b/crop/SimData.ipynb
index d767f96a117d43b08d4edac8e1000949d7f60a2c..c475a8013d724c6a2cd2fb9558d8e144741063a1 100644
--- a/crop/SimData.ipynb
+++ b/crop/SimData.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
@@ -11,28 +11,35 @@
"import pillow_heif\n",
"import pandas as pd\n",
"import numpy as np\n",
- "from random import randint"
+ "from torchvision.utils import save_image\n",
+ "import torchvision.transforms.functional as fn\n",
+ "import torchvision.transforms as transforms\n",
+ "import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
- "# TODO: overlap?\n",
- "def generate_data(bg: Image, beetle_set: list[Image], num_beetles_arr: list[int]):\n",
- " set_size = len(beetle_set)\n",
- " width, height = bg.size\n",
+ "# generates new dataset by pasting beetles and non-beetles in the same picture. \n",
+ "#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",
+ " set_size = len(beetle_set)\n",
+ " set_bg_size = len(bg)\n",
" sim_arr, coords_arr = [],[]\n",
"\n",
" for num_beetles in num_beetles_arr:\n",
- " bg_temp = bg.copy()\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",
" # get random beetle image\n",
- " beetle_id = np.random.randint(0, set_size-1)\n",
+ " beetle_id = np.random.randint(0, set_size)\n",
" beetle_img = beetle_set[beetle_id]\n",
" beetle_width, beetle_height = beetle_img.size\n",
"\n",
@@ -41,10 +48,14 @@
" y = np.random.randint(0, height - beetle_height)\n",
"\n",
" # get random beetle rotation\n",
- " angle = np.random.randint(0, 359)\n",
+ " angle = np.random.randint(0, 360)\n",
" beetle_img = beetle_img.rotate(angle, resample=Image.BICUBIC)\n",
"\n",
" bg_temp.paste(beetle_img, box=(x,y), mask=beetle_img)\n",
+ " \n",
+ " #centers x and y for YOLOv5 PyTorch label\n",
+ " x += beetle_width/2\n",
+ " y += beetle_height/2\n",
" beetle_coords.append((beetle_id, x, y, beetle_width, beetle_height, angle))\n",
" sim_arr.append(bg_temp)\n",
" coords_arr.append(beetle_coords)\n",
@@ -54,10 +65,11 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
+ "#???\n",
"def find_coeffs(source_coords, target_coords):\n",
" matrix = []\n",
" for s, t in zip(source_coords, target_coords):\n",
@@ -71,63 +83,1319 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
- "bg = Image.open(\"bg.png\")\n",
- "beetle_imgs = [Image.open(f) for f in glob.glob(\"beetles/*.png\")]\n",
+ "bg = Image.open(\"imgs/bg.png\")\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)\n",
- "#bg_flat"
+ "bg_flat = bg.transform(bg.size, Image.PERSPECTIVE, coeffs, Image.BICUBIC)"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
- "sim_img_arr, coords_arr = generate_data(bg_flat, beetle_imgs, [3])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "format\n",
- "0 x y width height (each value normalized to size of image)\n",
- "\n",
- "\n"
+ "beetle_counts = np.random.randint(0, 6, size=1250)\n",
+ "sim_img_arr, coords_arr = generate_data([bg_flat], beetles, beetle_counts)"
]
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
+ "#exports images and coordinates in YOLOv5 PyTorch format\n",
"def export(img_arr, coords_arr):\n",
" s = \"\"\n",
+ " image_set = \"train\"\n",
+ " change = len(img_arr)*0.8\n",
" for i in range(len(img_arr)):\n",
+ " s = \"\"\n",
+ " img = img_arr[i]\n",
" for coord in coords_arr[i]:\n",
" c, x, y, w, h, a = coord\n",
- " img = img_arr[i]\n",
" s += f\"{0} {x/img.size[0]} {y/img.size[1]} {w/img.size[0]} {h/img.size[1]}\\n\"\n",
- "\n",
- " with open(f\"sim{i}.txt\", \"w\") as f:\n",
+ " print(i)\n",
+ " if i > change:\n",
+ " image_set = \"test\"\n",
+ " with open(\"data/\" +image_set+ f\"/labels/sim{i}.txt\", \"w\") as f:\n",
" f.write(s)\n",
- " img.save(f\"sim{i}.png\")"
+ " img.save(\"data/\" +image_set+ f\"/images/sim{i}.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 63,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ ]
+ }
+ ],
"source": [
"export(sim_img_arr, coords_arr)"
]
@@ -137,12 +1405,96 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "format\n",
+ "0 x y width height (each value normalized to size of image)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RGBA\n",
+ "(2, 4)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from torchvision.utils import draw_bounding_boxes\n",
+ "import torch\n",
+ "import torchvision\n",
+ "transform = transforms.Compose([transforms.ToTensor()])\n",
+ "for i in range(len(sim_img_arr)):\n",
+ " print(sim_img_arr[i].mode)\n",
+ " \n",
+ " tensor = transform(sim_img_arr[i].convert(\"RGB\")).type(torch.uint8)\n",
+ " coords_list = np.zeros((len(coords_arr[i]), 4))\n",
+ " j=0\n",
+ " for k in coords_arr[i]:\n",
+ " c, x, y, w, h, a = k\n",
+ " coords_list[j, 0] = x\n",
+ " coords_list[j, 1] = y\n",
+ " coords_list[j, 2] = int(x+w)\n",
+ " coords_list[j, 3] = int(y+h)\n",
+ " j+=1\n",
+ " print(coords_list.shape)\n",
+ " draw_bounding_boxes(tensor, torch.Tensor(coords_list))\n",
+ " plt.imshow(tensor.permute(1,2,0)[:,:,0:3])\n",
+ " plt.show()\n",
+ " break\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ " \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",
+ " - perform auto cropping on arduino and include "
+ ]
}
],
"metadata": {
"kernelspec": {
- "display_name": "tf",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -156,9 +1508,8 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.9.12"
},
- "orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "5501471bf458387d76cdb9f487659a1abd30d9114d99ffb68b67c5dd3feef292"
diff --git a/crop/F2_3AUGUST2022.jpg b/crop/imgs/F2_3AUGUST2022.jpg
similarity index 100%
rename from crop/F2_3AUGUST2022.jpg
rename to crop/imgs/F2_3AUGUST2022.jpg
diff --git a/crop/bg.png b/crop/imgs/bg.png
similarity index 100%
rename from crop/bg.png
rename to crop/imgs/bg.png
diff --git a/crop/non_beetles/non_beetle0.png b/crop/non_beetles/non_beetle0.png
new file mode 100644
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diff --git a/crop/non_beetles/non_beetle1.png b/crop/non_beetles/non_beetle1.png
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diff --git a/crop/non_beetles/non_beetle2.png b/crop/non_beetles/non_beetle2.png
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diff --git a/crop/non_beetles/non_beetle3.png b/crop/non_beetles/non_beetle3.png
new file mode 100644
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diff --git a/crop/non_beetles/non_beetle4.png b/crop/non_beetles/non_beetle4.png
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diff --git a/crop/non_beetles/non_beetle5.png b/crop/non_beetles/non_beetle5.png
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diff --git a/crop/non_beetles/non_beetle6.png b/crop/non_beetles/non_beetle6.png
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diff --git a/yolov5_model/runs/train/6beetle_30_epoch/F1_curve.png b/yolov5_model/runs/train/6beetle_30_epoch/F1_curve.png
new file mode 100644
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diff --git a/yolov5_model/runs/train/6beetle_30_epoch/PR_curve.png b/yolov5_model/runs/train/6beetle_30_epoch/PR_curve.png
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diff --git a/yolov5_model/runs/train/6beetle_30_epoch/P_curve.png b/yolov5_model/runs/train/6beetle_30_epoch/P_curve.png
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diff --git a/yolov5_model/runs/train/6beetle_30_epoch/R_curve.png b/yolov5_model/runs/train/6beetle_30_epoch/R_curve.png
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diff --git a/yolov5_model/runs/train/6beetle_30_epoch/confusion_matrix.png b/yolov5_model/runs/train/6beetle_30_epoch/confusion_matrix.png
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diff --git a/yolov5_model/runs/train/6beetle_30_epoch/events.out.tfevents.1677460973.hal-dgx.5585.0 b/yolov5_model/runs/train/6beetle_30_epoch/events.out.tfevents.1677460973.hal-dgx.5585.0
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diff --git a/yolov5_model/runs/train/exp/hyp.yaml b/yolov5_model/runs/train/6beetle_30_epoch/hyp.yaml
similarity index 100%
rename from yolov5_model/runs/train/exp/hyp.yaml
rename to yolov5_model/runs/train/6beetle_30_epoch/hyp.yaml
diff --git a/yolov5_model/runs/train/6beetle_30_epoch/labels.jpg b/yolov5_model/runs/train/6beetle_30_epoch/labels.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..377708dc9d6775b655f6cc29def3941cced5cc0e
Binary files /dev/null and b/yolov5_model/runs/train/6beetle_30_epoch/labels.jpg differ
diff --git a/yolov5_model/runs/train/6beetle_30_epoch/labels_correlogram.jpg b/yolov5_model/runs/train/6beetle_30_epoch/labels_correlogram.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2c0a2e51387052ffebe9c1e5786f4a9007b751f6
Binary files /dev/null and b/yolov5_model/runs/train/6beetle_30_epoch/labels_correlogram.jpg differ
diff --git a/yolov5_model/runs/train/6beetle_30_epoch/opt.yaml b/yolov5_model/runs/train/6beetle_30_epoch/opt.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..066eafae1c55e07f3d7a67274d612ca4635caf7e
--- /dev/null
+++ b/yolov5_model/runs/train/6beetle_30_epoch/opt.yaml
@@ -0,0 +1,68 @@
+weights: yolov5m.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: 16
+imgsz: 1280
+rect: false
+resume: false
+nosave: false
+noval: false
+noautoanchor: false
+noplots: false
+evolve: null
+bucket: ''
+cache: null
+image_weights: false
+device: ''
+multi_scale: false
+single_cls: false
+optimizer: SGD
+sync_bn: false
+workers: 40
+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: null
+upload_dataset: false
+bbox_interval: -1
+artifact_alias: latest
+save_dir: runs/train/exp14
diff --git a/yolov5_model/runs/train/6beetle_30_epoch/results.csv b/yolov5_model/runs/train/6beetle_30_epoch/results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8b39527d47a000562ca85cd60866716162c6d75a
--- /dev/null
+++ b/yolov5_model/runs/train/6beetle_30_epoch/results.csv
@@ -0,0 +1,31 @@
+ epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
+ 0, 0.088528, 0.053824, 0, 0.36393, 0.69626, 0.43014, 0.12782, 0.066783, 0.024315, 0, 0.070476, 0.0032804, 0.0032804
+ 1, 0.069395, 0.026505, 0, 0.29405, 0.94205, 0.29515, 0.10084, 0.069764, 0.019618, 0, 0.040258, 0.0063955, 0.0063955
+ 2, 0.06626, 0.020283, 0, 0.38572, 0.76159, 0.39954, 0.11646, 0.06823, 0.017204, 0, 0.0098197, 0.0092906, 0.0092906
+ 3, 0.058401, 0.018056, 0, 0.44842, 0.80795, 0.59281, 0.26819, 0.053659, 0.016442, 0, 0.00901, 0.00901, 0.00901
+ 4, 0.050479, 0.01615, 0, 0.87631, 0.9702, 0.93333, 0.38929, 0.047041, 0.014822, 0, 0.00901, 0.00901, 0.00901
+ 5, 0.044084, 0.014618, 0, 0.99339, 0.99488, 0.99409, 0.53026, 0.031072, 0.013345, 0, 0.00868, 0.00868, 0.00868
+ 6, 0.038829, 0.01355, 0, 0.99945, 0.99669, 0.995, 0.68291, 0.035629, 0.011882, 0, 0.00835, 0.00835, 0.00835
+ 7, 0.035219, 0.013494, 0, 0.98506, 0.99669, 0.99395, 0.55846, 0.032584, 0.012667, 0, 0.00802, 0.00802, 0.00802
+ 8, 0.033027, 0.012771, 0, 1, 0.99639, 0.995, 0.59871, 0.030749, 0.011781, 0, 0.00769, 0.00769, 0.00769
+ 9, 0.03076, 0.012084, 0, 0.99978, 0.99669, 0.995, 0.68194, 0.027393, 0.010798, 0, 0.00736, 0.00736, 0.00736
+ 10, 0.027931, 0.011738, 0, 0.99969, 0.99669, 0.995, 0.69653, 0.025421, 0.010677, 0, 0.00703, 0.00703, 0.00703
+ 11, 0.026893, 0.010864, 0, 0.9997, 0.99669, 0.995, 0.64308, 0.02588, 0.010726, 0, 0.0067, 0.0067, 0.0067
+ 12, 0.025891, 0.010558, 0, 0.9997, 0.99669, 0.995, 0.73511, 0.021744, 0.0096614, 0, 0.00637, 0.00637, 0.00637
+ 13, 0.024502, 0.010285, 0, 0.99982, 0.99669, 0.995, 0.68417, 0.025434, 0.009995, 0, 0.00604, 0.00604, 0.00604
+ 14, 0.023452, 0.010378, 0, 0.99987, 0.99669, 0.995, 0.81139, 0.016016, 0.0091467, 0, 0.00571, 0.00571, 0.00571
+ 15, 0.021093, 0.0096709, 0, 0.99963, 0.99669, 0.995, 0.7844, 0.019651, 0.0093482, 0, 0.00538, 0.00538, 0.00538
+ 16, 0.020885, 0.0094017, 0, 0.99968, 0.99669, 0.995, 0.8273, 0.015273, 0.0086163, 0, 0.00505, 0.00505, 0.00505
+ 17, 0.019271, 0.0088992, 0, 0.99972, 0.99669, 0.995, 0.82915, 0.016877, 0.0085796, 0, 0.00472, 0.00472, 0.00472
+ 18, 0.019061, 0.0091101, 0, 0.99966, 0.99669, 0.995, 0.83786, 0.014578, 0.0085793, 0, 0.00439, 0.00439, 0.00439
+ 19, 0.017827, 0.0089283, 0, 0.99973, 0.99669, 0.995, 0.78222, 0.017715, 0.0094559, 0, 0.00406, 0.00406, 0.00406
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diff --git a/yolov5_model/runs/train/6beetle_50_epoch/hyp.yaml b/yolov5_model/runs/train/6beetle_50_epoch/hyp.yaml
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index 0000000000000000000000000000000000000000..fa80eb95531dba10a6c94a7651f781f8c99566be
--- /dev/null
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+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
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diff --git a/yolov5_model/runs/train/6beetle_50_epoch/opt.yaml b/yolov5_model/runs/train/6beetle_50_epoch/opt.yaml
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index 0000000000000000000000000000000000000000..1e2ae5a3723133ffae48a56f5160a70587fdf031
--- /dev/null
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+weights: yolov5m.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
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+ cls: 0.5
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+ obj: 1.0
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+ iou_t: 0.2
+ anchor_t: 4.0
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+imgsz: 1280
+rect: false
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+evolve: null
+bucket: ''
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+device: ''
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+single_cls: false
+optimizer: SGD
+sync_bn: false
+workers: 40
+project: runs/train
+name: exp
+exist_ok: false
+quad: false
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+patience: 100
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+- 0
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+seed: 0
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+entity: null
+upload_dataset: false
+bbox_interval: -1
+artifact_alias: latest
+save_dir: runs/train/exp15
diff --git a/yolov5_model/runs/train/6beetle_50_epoch/results.csv b/yolov5_model/runs/train/6beetle_50_epoch/results.csv
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--- /dev/null
+++ b/yolov5_model/runs/train/6beetle_50_epoch/results.csv
@@ -0,0 +1,51 @@
+ epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
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similarity index 100%
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new file mode 100644
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@@ -0,0 +1,28 @@
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diff --git a/yolov5_model/runs/train/exp/labels.jpg b/yolov5_model/runs/train/wrong_1beetle/labels.jpg
similarity index 100%
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diff --git a/yolov5_model/runs/train/exp/opt.yaml b/yolov5_model/runs/train/wrong_1beetle/opt.yaml
similarity index 100%
rename from yolov5_model/runs/train/exp/opt.yaml
rename to yolov5_model/runs/train/wrong_1beetle/opt.yaml
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similarity index 100%
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similarity index 100%
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+weights: yolov5m.pt
+cfg: ''
+data: /projects/akhot2/group-01-phys371-sp2023/yolov5_model/data/beetles.yaml
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