Finding Edge Cases in a Computer Vision Dataset

A Roboflow tutorial describes an active-learning workflow for identifying computer vision edge cases that standard validation metrics may overlook because production images often differ in lighting, texture, camera setup, occlusion, and defect rarity. Using fabric-defect detection as an example, it trains a baseline model on plain-background images, then applies the model to a separate set of unlabeled patterned-fabric images and flags samples with no detections or prediction confidence below 0.60. A Roboflow Workflow counts detections, finds minimum confidence, selectively sends flagged images to Google Gemini for explanations of likely visual challenges, annotates predictions, and exports results to a CSV for review. Users then filter flagged images, manually label them in Roboflow Annotate, add them to the training dataset, and retrain the same model for comparison. In the example, this targeted addition of difficult images increased mAP@50 from 65.7% to 71.4%, precision from 81.0% to 86.5%, recall from 72.2% to 77.3%, and F1 from 68.9% to 81.5%, illustrating how repeated production-data review can reduce missed detections and help models adapt to real-world distribution shifts.

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