Author(s): Asad Iqbal

Originally published on Towards AI.

Object Detection Model You Need to Know (And When to Use Each)

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After the lead, the article explains what object detection models do (classifying and localizing multiple objects) and contrasts their main design families: two-stage detectors (R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN), one-stage detectors (SSD, YOLO variants, RetinaNet, EfficientDet), and anchor-free approaches (CenterNet, FCOS, plus related keypoint-based methods). It then covers transformer-based set prediction models (DETR and variants like Deformable DETR, DINO, and D-FINE), discusses how these models avoid or reduce reliance on NMS, and highlights real-time and deployment-focused models (RT-DETR, lightweight edge models, and strategies for parameter/latency reductions). Finally, it addresses open-vocabulary and foundation detection systems that use language prompts for zero-shot generalization, and notes the trade-off between broader category coverage and higher zero-shot error compared with fully supervised detectors.

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Published via Towards AI