How does DETR reformulate object detection?
hardAnswer
- DETR (Carion 2020) treats detection as a set-prediction problem: a transformer decoder outputs a fixed set of N (>>true count) predictions in parallel, matched to ground-truth objects via bipartite Hungarian matching during training.
- No anchors, no NMS.
- Simple, elegant, but slow to converge (500+ epochs) and struggles on small objects.
- Deformable-DETR fixes both by using deformable attention and multi-scale features.
Check yourself — multiple choice
- DETR uses anchors and NMS
- Transformer decoder outputs a fixed set of predictions matched to GT via Hungarian — no anchors, no NMS
- Same as Faster R-CNN
- Slower than Faster R-CNN inference
DETR: set prediction via transformer + Hungarian matching → no anchors, no NMS.
#detection#computer-vision#transformers
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