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Craig
2025-04-12 10:34:10 +01:00
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@@ -20,12 +20,12 @@ This list outlines the steps required to complete the Torchvision Finetuning pro
## Phase 2: Data Handling & Model
- [ ] Implement `PennFudanDataset` class in `utils/data_utils.py`.
- [ ] `__init__`: Load image and mask paths.
- [ ] `__getitem__`: Load image/mask, parse masks, generate targets (boxes, labels, masks, image_id, area, iscrowd), apply transforms.
- [ ] `__len__`: Return dataset size.
- [ ] Implement `get_transform(train)` function in `utils/data_utils.py` (using `torchvision.transforms.v2`).
- [ ] Implement `collate_fn(batch)` function in `utils/data_utils.py`.
- [x] Implement `PennFudanDataset` class in `utils/data_utils.py`.
- [x] `__init__`: Load image and mask paths.
- [x] `__getitem__`: Load image/mask, parse masks, generate targets (boxes, labels, masks, image_id, area, iscrowd), apply transforms.
- [x] `__len__`: Return dataset size.
- [x] Implement `get_transform(train)` function in `utils/data_utils.py` (using `torchvision.transforms.v2`).
- [x] Implement `collate_fn(batch)` function in `utils/data_utils.py`.
- [ ] Implement `get_maskrcnn_model(num_classes, ...)` function in `models/detection.py`.
- [ ] Load pre-trained Mask R-CNN (`maskrcnn_resnet50_fpn_v2`).
- [ ] Replace box predictor head (`FastRCNNPredictor`).