README_origin.md

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<h2 align="center">RT-DETR: DETRs Beat YOLOs on Real-time Object Detection</h2>
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---

<div align="center">
  <img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/0ede1dc1-a854-43b6-9986-cf9090f11a61" width=500 >
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This is the official implementation of the paper "[DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069)".


## 🚀 Updates
- \[2024.02.27\] Our work has been accepted to CVPR 2024!
- \[2024.01.23\] Fix difference on data augmentation with paper in rtdetr_pytorch [#84](https://github.com/lyuwenyu/RT-DETR/commit/5dc64138e439247b4e707dd6cebfe19d8d77f5b1).
- \[2023.11.07\] Add pytorch ✅ *rtdetr_r34vd* for requests [#107](https://github.com/lyuwenyu/RT-DETR/issues/107), [#114](https://github.com/lyuwenyu/RT-DETR/issues/114).
- \[2023.11.05\] Upgrade the logic of `remap_mscoco_category` to facilitate training of custom datasets, see detils in [*Train custom data*](./rtdetr_pytorch/) part. [#81](https://github.com/lyuwenyu/RT-DETR/commit/95fc522fd7cf26c64ffd2ad0c622c392d29a9ebf).
- \[2023.10.23\] Add [*discussion for deployments*](https://github.com/lyuwenyu/RT-DETR/issues/95), supported onnxruntime, TensorRT, openVINO.
- \[2023.10.12\] Add tuning code for pytorch version, now you can tuning rtdetr based on pretrained weights.
- \[2023.09.19\] Upload ✅ [*pytorch weights*](https://github.com/lyuwenyu/RT-DETR/issues/42) convert from paddle version.
- \[2023.08.24] Release RT-DETR-R18 pretrained models on objects365. *49.2 mAP* and *217 FPS*.
- \[2023.08.22\] Upload ✅ [*rtdetr_pytorch*](./rtdetr_pytorch/) source code. Please enjoy it!
- \[2023.08.15\] Release RT-DETR-R101 pretrained models on objects365. *56.2 mAP* and *74 FPS*.
- \[2023.07.30\] Release RT-DETR-R50 pretrained models on objects365. *55.3 mAP* and *108 FPS*.
- \[2023.07.28\] Fix some bugs, and add some comments. [1](https://github.com/lyuwenyu/RT-DETR/pull/14), [2](https://github.com/lyuwenyu/RT-DETR/commit/3b5cbcf8ae3b907e6b8bb65498a6be7c6736eabc).
- \[2023.07.13\] Upload ✅ [*training logs on coco*](https://github.com/lyuwenyu/RT-DETR/issues/8).
- \[2023.05.17\] Release RT-DETR-R18, RT-DETR-R34, RT-DETR-R50-m（example for scaled).
- \[2023.04.17\] Release RT-DETR-R50, RT-DETR-R101, RT-DETR-L, RT-DETR-X.

## 📍 Implementations
- 🔥 RT-DETR paddle: [code](./rtdetr_paddle), [weights](./rtdetr_paddle)
- 🔥 RT-DETR pytorch: [code](./rtdetr_pytorch), [weights](./rtdetr_pytorch)


| Model | Epoch | Input shape | Dataset | $AP^{val}$ | $AP^{val}_{50}$| Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS)
|:---:|:---:|:---:| :---:|:---:|:---:|:---:|:---:|:---:|
| RT-DETR-R18 | 6x | 640 | COCO | 46.5 | 63.8 | 20 | 60 | 217 |
| RT-DETR-R34 | 6x | 640 | COCO | 48.9 | 66.8 | 31 | 92 | 161 |
| RT-DETR-R50-m | 6x | 640 | COCO | 51.3 | 69.6 | 36 | 100 | 145 |
| RT-DETR-R50 | 6x |  640 | COCO | 53.1 | 71.3 | 42 | 136 | 108 |
| RT-DETR-R101 | 6x | 640 | COCO | 54.3 | 72.7 | 76 | 259 | 74 |
| RT-DETR-HGNetv2-L | 6x | 640 | COCO | 53.0 | 71.6 | 32 | 110 | 114 |
| RT-DETR-HGNetv2-X | 6x | 640 | COCO | 54.8 | 73.1 | 67 | 234 | 74 |
| RT-DETR-R18 | 5x | 640 | COCO + Objects365 | **49.2** | **66.6** | 20 | 60 | **217** |
| RT-DETR-R50 | 2x | 640 | COCO + Objects365 | **55.3** | **73.4** | 42 | 136 | **108** |
| RT-DETR-R101 | 2x | 640 | COCO + Objects365 | **56.2** | **74.6** | 76 | 259 | **74** |

**Notes:**
- `COCO + Objects365` in the table means finetuned model on COCO using pretrained weights trained on Objects365.


## 💡 Introduction
We propose a **R**eal-**T**ime **DE**tection **TR**ansformer (RT-DETR, aka RTDETR), the first real-time end-to-end object detector to our best knowledge. Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU, outperforming previously advanced YOLOs in both speed and accuracy. Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP.
<div align="center">
  <img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/c211a164-ddce-4084-8b71-fb73f29f363b" width=500 >
</div>

## 🦄 Performance

### 🏕️ Complex Scenarios
<div align="center">
  <img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/52743892-68c8-4e53-b782-9f89221739e4" width=500 >
</div>

### 🌋 Difficult Conditions
<div align="center">
  <img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/213cf795-6da6-4261-8549-11947292d3cb" width=500 >
</div>

## Citation
If you use `RT-DETR` in your work, please use the following BibTeX entries:
```
@misc{lv2023detrs,
      title={DETRs Beat YOLOs on Real-time Object Detection},
      author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen},
      year={2023},
      eprint={2304.08069},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```