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Hey Eric, overall really good stuff! I left a few comments. |
Ansh-Gandhi
requested changes
Mar 7, 2026
| MODEL_PATH = "checkpoints/best_model.pth" | ||
| THRESHOLD = 0.9 | ||
| OUTPUT_PATH = "results/collage.jpg" | ||
| WINDOW_SIZES = [256, 512] |
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Should this window size be 128 x 256?
src/common/voc_dataset.py
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| def _parse_annotations(self) -> None: | ||
| """Parse all XML annotations and create sample list.""" | ||
| """Parse annotations - only include target class and one negative class.""" | ||
| TARGET_CLASS_IDX = 14 # person |
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I am not 100% sure on this but there is a chance the labels may have to be 0 and 1 for the classification due to pytorch expecting that instead.
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This should be okay because I convert to 0/1 labels in the training loop, here for reference:
target-object-localization/src/training.py
Lines 141 to 143 in 1dcfa34
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We ran into issues that stemmed from the fact that the model was trained on cropped bounding boxes of the target class but ran inference on full resized images, causing a train/inference mismatch and significant amount of false positives.
Fixed by adding a sliding window approach (128, 256 px). Also noticed potential issues with the data imbalance, so I balanced the training data to just the "target" class + one other class (instead of the full 19 classes), this produces very decent results.