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fix: pass ruff lint+format, clean README, remove stray script
Priya Monishafa0ce5b
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results: Section 5 complete — all 4 CNNs trained, ResNet50 best at 65.5%
Priya MonishaSection 5 final results: - VGG16: 59.5% (architecture ceiling) - MobileNetV2: 56.7% (backbone lr=1e-5 too conservative) - EfficientNetB0: 58.9% (backbone lr=1e-5 too conservative) - ResNet50: 65.5% test / 63.6% val (data volume bottleneck, 1464 params/img) Pipeline improvements committed alongside results: - dataset.py: create_stratified_split() — random stratified resplit, no sequential bias - loader.py: quality gates 48px→80px, 2% area; compute_overlap_ratio; check_person_overlap - model_factory.py: unfreeze_efficientnet_phase2() added - 01_data_acquisition.py: chair person-overlap filter, MAX_ITER 80k→150k Next: Section 6 YOLOv8 detection training1a28594
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fix: remove emoji from loader.py print (Windows cp1252 encoding error)
Priya Monisha261ffe4 -
section-2: dataset acquisition — loader, dataset, helpers, 01_data_acquisition notebook
Priya MonishaKey corrections from mentor notebook analysis: - config.py: replaced COCO original IDs (1-90) with HF 0-indexed IDs (0-79) via SELECTED_CLASSES dict and HF_CATEGORY_TO_CLASS_IDX reverse mapping - Fixed to 25 classes (mentor's notebook had 26 by accidentally including 'train' vehicle) - Confirmed bbox format: [x,y,w,h] absolute pixels (not [xmin,ymin,xmax,ymax]) - No trust_remote_code needed for detection-datasets/coco src/utils/helpers.py: - NumpyEncoder (torch.Tensor → np.bool_ → np.integer → np.floating → np.ndarray) - save_json/load_json with NumpyEncoder - HF Hub helpers: upload_model_to_hub, download_model_from_hub (HfApi().upload_file) - create_hub_repo with exist_ok=True src/data/loader.py: - verify_coco_mapping() using SELECTED_CLASSES + HF_CATEGORY_TO_CLASS_IDX - Checkpoint/resume (mentor had none — Colab disconnect = start over) - save_classification_crop: [x,y,w,h] → PIL.crop((x,y,x+w,y+h)) → resize 224×224 - save_detection_sample: [x,y,w,h] → YOLO normalized format - Pr…9b0ae38