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Accuracy Is a Trap: How to Actually Choose a Computer Vision Model
Most teams pick computer vision models based on benchmark accuracy and ignore failure modes. Dr. Satya Mallick explains why that breaks production systems — and what to audit instead. Models discussed: • YOLOv26 (object detection) • RF-DETR (transformer-based detection) • Pose estimation models on regular vs out-of-distribution scenes ...
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