FishServer/FishMeasure/detection/README.md

Fish Detection YOLO Pipeline

We plan to train our own YOLO model specifically for fish detection. The workflow and TODOs are:

1. Labeling

   • Use labelme to annotate selected fish images.
   • Convert annotations to YOLO format (e.g., using labelme2yolo or a custom script).

2. Training & Testing Scripts

   • Implement train_yolo.py that:
     • Loads the annotated dataset via a configurable dataloader.
     • Supports dataset splits (train/val/test) and typical YOLO hyperparameters.
     • Saves checkpoints and training logs.
   • Implement test_yolo.py that:
     • Loads trained checkpoints.
     • Runs inference/evaluation on validation or test sets.
     • Outputs metrics (precision/recall/mAP) and visualizes detections.

3. Data Loaders

   • Provide reusable dataloaders that:
     • Handle YOLO-format labels and image augmentations.
     • Support batching, shuffling, and CPU/GPU prefetching.
     • Can be shared between training and testing scripts.

TODO Summary

• Annotate images with labelme.
• Create train_yolo.py with dataloaders + training logic.
• Create test_yolo.py with dataloaders + evaluation/visualization.