Initial commit: FishServer monorepo (FishAction, FishMeasure, fish_api)

Made-with: Cursor

FishMeasure/detection/README.md

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+## 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.