transfer/transfer.py

@@ -37,7 +37,7 @@ for file in os.listdir(dir):
         save_paths.append(dest_path)
 
 #load model
-model_path = 'yolov5_model/runs/train/20beetle_40-non_20dirt_bkg_overlap/weights/best.pt'
+model_path = '../yolov5_model/runs/train/20beetle_40-non_20dirt_bkg_overlap/weights/best.pt'
 model = torch.hub.load('ultralytics/yolov5', 'custom', model_path)
 model.eval()
 

yolov5_model/data/beetles_real.yaml

-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

yolov5_model/runs/detect/README.md

+The different directories contain the predicted locations of western corn rootworm (WCR) beetles using the YOLOv5x model trained on the simulated dataset. The content of each file is summarized below.
+
+- `arduino_1280_yolov5x`: Predictions on images taken by the Arducam and using the YOLOv5x model with an image size of 1280
+- `arduino_640_yolov5x`: Predictions on images taken by the Arducam and using the YOLOv5x model with an image size of 640
+- `real_1280_yolov5x`: Predictions on images taken by an iPhone and using the YOLOv5x model with an image size of 1280
+- `real_640_yolov5x`: Predictions on images taken by an iPhone and using the YOLOv5x model with an image size of 640
+- `sim_1280_yolov5x`: Predictions on images from the test simulated dataset and using the YOLOv5x model with an image size of 1280
+- `sim_640_yolov5x`: Predictions on images from the test simulated dataset and using the YOLOv5x model with an image size of 640