readme: ---
  license: apache-2.0
  ---
  
  ## Model Description
  This **DAMO-YOLO-M** model is a medium-size object detection model with fast inference speed and high accuracy, trained by **DAMO-YOLO**. 
  DAMO-YOLO is a fast and accurate object detection method, which is developed by TinyML Team from Alibaba DAMO Data Analytics and Intelligence Lab. And it achieves a higher performance than state-of-the-art YOLO series. DAMO-YOLO is extend from YOLO but with some new techs, including Neural Architecture Search (NAS) backbones, efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. For more details, please refer to our [Arxiv Report](https://arxiv.org/abs/2211.15444) and [Github Code](https://github.com/tinyvision/DAMO-YOLO). Moreover, here you can find not only powerful models, but also highly efficient training strategies and complete tools from training to deployment.
  
  ## Chinese Web Demo
  - We also provide Chinese Web Demo on ModelScope, including [DAMO-YOLO-T](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-t/summary), [DAMO-YOLO-S](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo/summary), [DAMO-YOLO-M](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-m/summary).
  
  ## Datasets
  The model is trained on COCO2017.
  
  ## Model Usage
  The usage guideline can be found in our [Quick Start Tutorial](https://github.com/tinyvision/DAMO-YOLO).
  
  ## Model Evaluation
  |Model |size |mAP<sup>val<br>0.5:0.95 | Latency T4<br>TRT-FP16-BS1| FLOPs<br>(G)| Params<br>(M)| Download |
  | ------        |:---: | :---:     |:---:|:---: | :---: | :---:|
  |[DAMO-YOLO-T](./configs/damoyolo_tinynasL20_T.py) | 640 | 41.8  | 2.78  | 18.1  | 8.5  |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL20_T_418.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL20_T_418.onnx)  |
  |[DAMO-YOLO-T*](./configs/damoyolo_tinynasL20_T.py) | 640 | 43.0  | 2.78  | 18.1  | 8.5  |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL20_T.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL20_T.onnx)  |
  |[DAMO-YOLO-S](./configs/damoyolo_tinynasL25_S.py) | 640 | 45.6  | 3.83  | 37.8  | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL25_S_456.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL25_S_456.onnx)  |
  |[DAMO-YOLO-S*](./configs/damoyolo_tinynasL25_S.py) | 640 | 46.8  | 3.83  | 37.8  | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL25_S.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL25_S.onnx)  |
  |[DAMO-YOLO-M](./configs/damoyolo_tinynasL35_M.py) | 640 | 48.7  | 5.62  | 61.8  | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL35_M_487.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL35_M_487.onnx)|
  |[DAMO-YOLO-M*](./configs/damoyolo_tinynasL35_M.py) | 640 | 50.0  | 5.62  | 61.8  | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL35_M.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL35_M.onnx)|
  
  - We report the mAP of models on COCO2017 validation set, with multi-class NMS.
  - The latency in this table is measured without post-processing.
  - \* denotes the model trained with distillation.
  
  ## Cite DAMO-YOLO
  If you use DAMO-YOLO in your research, please cite our work by using the following BibTeX entry:
  
  ```latex
   @article{damoyolo,
     title={DAMO-YOLO: A Report on Real-Time Object Detection Design},
     author={Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang and Xiuyu Sun},
     journal={arXiv preprint arXiv:2211.15444v2},
     year={2022},
   }
  ```
  