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section-4: preprocessing + augmentation — verified end-to-end
Priya Monishasrc/data/preprocessor.py: - validate_yolo_annotations(): 1550 train + 375 val files, 13758 annotations all valid Fixed: assert <= 1.0 not < 1.0 (bbox_to_yolo clamps to min(1.0,...) so boundary values valid) - create_yolo_data_yaml(): absolute paths, works in terminal + Colab - verify_dataset_structure(): counts per split, detects image/label mismatches src/data/augmentor.py: - get_train_transforms(): flip, rotate±15, colorjitter, zoomout, resize 224, ToImage, Normalize(ImageNet) - get_eval_transforms(): resize + normalize only - denormalize(): for visualization - torch wrapped in try/except (local fbgemm.dll issue) notebooks/03_preprocessing.py: - Dataset verified: 2500 classification, 1925 detection, labels match images - YOLO annotations validated: 13758 annotations, all in valid range - data.yaml regenerated with current absolute paths - Augmented sample images saved to docs/figures/ - Tensor shapes confirmed: (3, 224, 224) float32, range ~[-2.1, 2.6] after ImageNet norm2ea8a2d