Real-time DEtection Transformer (RT-DETR) landed in transformers 🤩 with Apache 2.0 license 😍
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Real-time DEtection Transformer (RT-DETR) landed in transformers 🤩 with Apache 2.0 license 😍
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🔖 models: 
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🔖 models: 
https://www.gravatar.com/avatar/a2cf4e4900d33c5e795f772e8327843b?d=retro&size=100
https://huggingface.co/PekingU
PekingU
org
resource
https://huggingface.co/PekingU
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🔖 demo: 
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🔖 demo: 
https://huggingface.co/spaces/merve/RT-DETR-tracking-coco
merve/RT-DETR-tracking-coco
space
resource
https://huggingface.co/spaces/merve/RT-DETR-tracking-coco
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📝 paper: 
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📝 paper: 
DETRs Beat YOLOs on Real-time Object Detection (2304.08069)
https://huggingface.co/papers/2304.08069
2304.08069
paper
resource
https://huggingface.co/papers/2304.08069
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📖 notebook: 
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📖 notebook: 
https://github.com/merveenoyan/example_notebooks/blob/main/RT_DETR_Notebook.ipynb
https://github.com/merveenoyan/example_notebooks/blob/main/RT_DETR_Notebook.ipynb
link
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YOLO models are known to be super fast for real-time computer vision, but they have a downside with being volatile to NMS 🥲
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YOLO models are known to be super fast for real-time computer vision, but they have a downside with being volatile to NMS 🥲
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Transformer-based models on the other hand are computationally not as efficient 🥲
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Transformer-based models on the other hand are computationally not as efficient 🥲
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Isn't there something in between? Enter RT-DETR!
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Isn't there something in between? Enter RT-DETR!
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The authors combined CNN backbone, multi-stage hybrid decoder (combining convs and attn) with a transformer decoder. In the paper, authors also claim one can adjust speed by changing decoder layers without retraining altogether. 
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The authors combined CNN backbone, multi-stage hybrid decoder (combining convs and attn) with a transformer decoder. In the paper, authors also claim one can adjust speed by changing decoder layers without retraining altogether. 
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The authors find out that the model performs better in terms of speed and accuracy compared to the previous state-of-the-art. 🤩
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The authors find out that the model performs better in terms of speed and accuracy compared to the previous state-of-the-art. 🤩
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Real-time DEtection Transformer (RT-DETR) landed in transformers 🤩 with Apache 2.0 license 😍

🔖 models: https://huggingface.co/PekingU
🔖 demo: https://huggingface.co/spaces/merve/RT-DETR-tracking-coco
📝 paper: https://huggingface.co/papers/2304.08069
📖 notebook: https://github.com/merveenoyan/example_notebooks/blob/main/RT_DETR_Notebook.ipynb

YOLO models are known to be super fast for real-time computer vision, but they have a downside with being volatile to NMS 🥲

Transformer-based models on the other hand are computationally not as efficient 🥲

Isn't there something in between? Enter RT-DETR!

The authors combined CNN backbone, multi-stage hybrid decoder (combining convs and attn) with a transformer decoder. In the paper, authors also claim one can adjust speed by changing decoder layers without retraining altogether. 
The authors find out that the model performs better in terms of speed and accuracy compared to the previous state-of-the-art. 🤩

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👍
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