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🚀 Transformers are not here to take part but take over... and down goes real-time object detection! 💥
🚀 Transformers are not here to take part but take over... and down goes real-time object detection! 💥
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Enter Real-time DEtection Transformer (RT-DETR) 🦾 as suggested capable of real-time object detection. 🎯
Enter Real-time DEtection Transformer (RT-DETR) 🦾 as suggested capable of real-time object detection. 🎯
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Object DEtection Transformer (DETR) is not new (
Object DEtection Transformer (DETR) is not new (
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@Meta
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 did it eons ago) but it had the issue of every other transformer, high computational cost 💸
 did it eons ago) but it had the issue of every other transformer, high computational cost 💸
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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 🏎️
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 🏎️
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Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. ⚡
Gist is RT-DETR speeds up object detection by redesigning its encoder to process features more efficiently and selecting higher quality initial object queries. ⚡
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It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. ⚖️
It also allows adjusting the number of decoder layers to balance speed and accuracy for different real-time scenarios. ⚖️
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This makes RT-DETR faster and more accurate than previous YOLO models. 🏆
This makes RT-DETR faster and more accurate than previous YOLO models. 🏆
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How much better😎/faster? ⏱️
How much better😎/faster? ⏱️
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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. 🚀✨
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. 🚀✨
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📄 Paper: 
📄 Paper: 
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https://huggingface.co/papers/2304.08069
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2304.08069
https://huggingface.co/papers/2304.08069
DETRs Beat YOLOs on Real-time Object Detection (2304.08069)
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🧠 Models: 
🧠 Models: 
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https://huggingface.co/models?search=pekingu/rt-detr
https://huggingface.co/models?search=pekingu/rt-detr
🚀 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: https://huggingface.co/papers/2304.08069

🧠 Models: https://huggingface.co/models?search=pekingu/rt-detr
🚀
merterbak clem osanseviero louisbrulenaudet
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blender66cat Artificial-superintelligence
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