支持yoloX的各种方式的部署

<div align="center"><img src="assets/logo.png" width="350"></div> <img src="assets/demo.png" > ## Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/2107.08430). <img src="assets/git_fig.png" width="1000" > ## Updates!! * 【2021/07/28】 We fix the fatal error of [memory leak](https://github.com/Megvii-BaseDetection/YOLOX/issues/103) * 【2021/07/26】 We now support [MegEngine](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/MegEngine) deployment. * 【2021/07/20】 We have released our technical report on [Arxiv](https://arxiv.org/abs/2107.08430). ## Comming soon - [ ] YOLOX-P6 and larger model. - [ ] Objects365 pretrain. - [ ] Transformer modules. - [ ] More features in need. ## Benchmark #### Standard Models. |Model |size |mAP<sup>test<br>0.5:0.95 | Speed V100<br>(ms) | Params<br>(M) |FLOPs<br>(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | :----: | |[YOLOX-s](./exps/default/yolox_s.py) |640 |39.6 |9.8 |9.0 | 26.8 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EW62gmO2vnNNs5npxjzunVwB9p307qqygaCkXdTO88BLUg?e=NMTQYw)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.pth) | |[YOLOX-m](./exps/default/yolox_m.py) |640 |46.4 |12.3 |25.3 |73.8| [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/ERMTP7VFqrVBrXKMU7Vl4TcBQs0SUeCT7kvc-JdIbej4tQ?e=1MDo9y)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_m.pth) | |[YOLOX-l](./exps/default/yolox_l.py) |640 |50.0 |14.5 |54.2| 155.6 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EWA8w_IEOzBKvuueBqfaZh0BeoG5sVzR-XYbOJO4YlOkRw?e=wHWOBE)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_l.pth) | |[YOLOX-x](./exps/default/yolox_x.py) |640 |**51.2** | 17.3 |99.1 |281.9 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EdgVPHBziOVBtGAXHfeHI5kBza0q9yyueMGdT0wXZfI1rQ?e=tABO5u)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_x.pth) | |[YOLOX-Darknet53](./exps/default/yolov3.py) |640 | 47.4 | 11.1 |63.7 | 185.3 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EZ-MV1r_fMFPkPrNjvbJEMoBLOLAnXH-XKEB77w8LhXL6Q?e=mf6wOc)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_darknet53.pth) | #### Light Models. |Model |size |mAP<sup>val<br>0.5:0.95 | Params<br>(M) |FLOPs<br>(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | |[YOLOX-Nano](./exps/default/nano.py) |416 |25.3 | 0.91 |1.08 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EdcREey-krhLtdtSnxolxiUBjWMy6EFdiaO9bdOwZ5ygCQ?e=yQpdds)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_nano.pth) | |[YOLOX-Tiny](./exps/default/yolox_tiny.py) |416 |32.8 | 5.06 |6.45 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EbZuinX5X1dJmNy8nqSRegABWspKw3QpXxuO82YSoFN1oQ?e=Q7V7XE)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_tiny_32dot8.pth) | ## Quick Start <details> <summary>Installation</summary> Step1. Install YOLOX. ```shell git clone git@github.com:Megvii-BaseDetection/YOLOX.git cd YOLOX pip3 install -U pip && pip3 install -r requirements.txt pip3 install -v -e . # or python3 setup.py develop ``` Step2. Install [apex](https://github.com/NVIDIA/apex). ```shell # skip this step if you don't want to train model. git clone https://github.com/NVIDIA/apex cd apex pip3 install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ ``` Step3. Install [pycocotools](https://github.com/cocodataset/cocoapi). ```shell pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' ``` </details> <details> <summary>Demo</summary> Step1. Download a pretrained model from the benchmark table. Step2. Use either -n or -f to specify your detector's config. For example: ```shell python tools/demo.py image -n yolox-s -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu] ``` or ```shell python tools/demo.py image -f exps/default/yolox_s.py -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu] ``` Demo for video: ```shell python tools/demo.py video -n yolox-s -c /path/to/your/yolox_s.pth --path /path/to/your/video --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu] ``` </details> <details> <summary>Reproduce our results on COCO</summary> Step1. Prepare COCO dataset ```shell cd <YOLOX_HOME> ln -s /path/to/your/COCO ./datasets/COCO ``` Step2. Reproduce our results on COCO by specifying -n: ```shell python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o yolox-m yolox-l yolox-x ``` * -d: number of gpu devices * -b: total batch size, the recommended number for -b is num-gpu * 8 * --fp16: mixed precision training **Multi Machine Training** We also support multi-nodes training. Just add the following args: * --num\_machines: num of your total training nodes * --machine\_rank: specify the rank of each node When using -f, the above commands are equivalent to: ```shell python tools/train.py -f exps/default/yolox-s.py -d 8 -b 64 --fp16 -o exps/default/yolox-m.py exps/default/yolox-l.py exps/default/yolox-x.py ``` </details> <details> <summary>Evaluation</summary> We support batch testing for fast evaluation: ```shell python tools/eval.py -n yolox-s -c yolox_s.pth -b 64 -d 8 --conf 0.001 [--fp16] [--fuse] yolox-m yolox-l yolox-x ``` * --fuse: fuse conv and bn * -d: number of GPUs used for evaluation. DEFAULT: All GPUs available will be used. * -b: total batch size across on all GPUs To reproduce speed test, we use the following command: ```shell python tools/eval.py -n yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001 --fp16 --fuse yolox-m yolox-l yolox-x ``` </details> <details open> <summary>Tutorials</summary> * [Training on custom data](docs/train_custom_data.md). </details> ## Deployment 1. [MegEngine in C++ and Python](./demo/MegEngine) 2. [ONNX export and an ONNXRuntime](./demo/ONNXRuntime) 3. [TensorRT in C++ and Python](./demo/TensorRT) 4. [ncnn in C++ and Java](./demo/ncnn) 5. [OpenVINO in C++ and Python](./demo/OpenVINO) ## Third-party resources * The ncnn android app with video support: [ncnn-android-yolox](https://github.com/FeiGeChuanShu/ncnn-android-yolox) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu) * YOLOX with Tengine support: [Tengine](https://github.com/OAID/Tengine/blob/tengine-lite/examples/tm_yolox.cpp) from [BUG1989](https://github.com/BUG1989) * YOLOX + ROS2 Foxy: [YOLOX-ROS](https://github.com/Ar-Ray-code/YOLOX-ROS) from [Ar-Ray](https://github.com/Ar-Ray-code) * YOLOX Deploy DeepStream: [YOLOX-deepstream](https://github.com/nanmi/YOLOX-deepstream) from [nanmi](https://github.com/nanmi) * YOLOX ONNXRuntime C++ Demo: [lite.ai](https://github.com/DefTruth/lite.ai/blob/main/ort/cv/yolox.cpp) from [DefTruth](https://github.com/DefTruth) * Converting darknet or yolov5 datasets to COCO format for YOLOX: [YOLO2COCO](https://github.com/RapidAI/YOLO2COCO) from [Daniel](https://github.com/znsoftm) ## Cite YOLOX If you use YOLOX in