## 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.