Why DETRs are replacing YOLOs for real-time object detection
12 hours ago
- #computer-vision
- #object-detection
- #transformers
- Real-time object detection is crucial for systems interpreting visual data efficiently.
- D-Fine, a DETR family model, replaces older CNN-based detectors for better accuracy and speed.
- DETR models offer better licensing (Apache 2.0) compared to YOLO's restrictive AGPL-3.0.
- DETRs treat detection as a set-prediction problem, eliminating hand-crafted components like NMS.
- Modern GPUs optimize attention operations, making transformers suitable for real-time applications.
- DETRs adapt well to new datasets and benefit from pre-training on datasets like COCO.
- DETR architecture includes a CNN backbone, transformer encoder-decoder, and direct set-prediction.
- Enhancements like Deformable DETR and DN-DETR improved DETR's performance and training convergence.
- RT-DETR and LW-DETR are leading DETR variants, with D-Fine and RF-DETR setting new standards.
- D-Fine scales better with model size, while RF-DETR excels in smaller, faster models.
- DETRs outperform YOLO in accuracy and are preferred for high-speed, high-accuracy scenarios.