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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.