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🚀 Transformers are not here to take part but take over... and down goes real-time object detection! 💥
Enter Real-time DEtection Transformer (RT-DETR) 🦾 as suggested capable of real-time object detection. 🎯
Object DEtection Transformer (DETR) is not new ( @Meta did it eons ago) but it had the issue of every other transformer, high computational cost 💸
RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed 🏎️
Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. ⚡
It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. ⚖️
This makes RT-DETR faster and more accurate than previous YOLO models. 🏆
How much better😎/faster? ⏱️
RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. 🚀✨
📄 Paper: DETRs Beat YOLOs on Real-time Object Detection (2304.08069)
🧠 Models: https://huggingface.co/models?search=pekingu/rt-detr
Enter Real-time DEtection Transformer (RT-DETR) 🦾 as suggested capable of real-time object detection. 🎯
Object DEtection Transformer (DETR) is not new ( @Meta did it eons ago) but it had the issue of every other transformer, high computational cost 💸
RT-DETR brings an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed 🏎️
Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. ⚡
It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. ⚖️
This makes RT-DETR faster and more accurate than previous YOLO models. 🏆
How much better😎/faster? ⏱️
RT-DETR-R50 achieved 53.1% AP on COCO and 108 FPS on a T4 GPU, while RT-DETR-R101 achieved 54.3% AP and 74 FPS, outperforming advanced YOLO models in both speed and accuracy. 🚀✨
📄 Paper: DETRs Beat YOLOs on Real-time Object Detection (2304.08069)
🧠 Models: https://huggingface.co/models?search=pekingu/rt-detr