29 lines
1.2 KiB
Markdown
29 lines
1.2 KiB
Markdown
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## Fish Detection YOLO Pipeline
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We plan to train our own YOLO model specifically for fish detection. The workflow and TODOs are:
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1. **Labeling**
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- Use `labelme` to annotate selected fish images.
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- Convert annotations to YOLO format (e.g., using `labelme2yolo` or a custom script).
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2. **Training & Testing Scripts**
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- Implement `train_yolo.py` that:
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- Loads the annotated dataset via a configurable dataloader.
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- Supports dataset splits (train/val/test) and typical YOLO hyperparameters.
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- Saves checkpoints and training logs.
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- Implement `test_yolo.py` that:
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- Loads trained checkpoints.
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- Runs inference/evaluation on validation or test sets.
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- Outputs metrics (precision/recall/mAP) and visualizes detections.
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3. **Data Loaders**
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- Provide reusable dataloaders that:
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- Handle YOLO-format labels and image augmentations.
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- Support batching, shuffling, and CPU/GPU prefetching.
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- Can be shared between training and testing scripts.
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> TODO Summary
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> - [ ] Annotate images with `labelme`.
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> - [ ] Create `train_yolo.py` with dataloaders + training logic.
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> - [ ] Create `test_yolo.py` with dataloaders + evaluation/visualization.
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