# YOLOX **Repository Path**: linkchainiii/YOLOX ## Basic Information - **Project Name**: YOLOX - **Description**: No description available - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-07-21 - **Last Updated**: 2021-07-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
## 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). ## Updates!! * 【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 |mAPtest
0.5:0.95 | Speed V100
(ms) | Params
(M) |FLOPs
(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | :----: | |[YOLOX-s](./exps/default/yolox_s.py) |640 |39.6 |9.8 |9.0 | 26.8 | [Download](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EW62gmO2vnNNs5npxjzunVwB9p307qqygaCkXdTO88BLUg?e=NMTQYw) | |[YOLOX-m](./exps/default/yolox_m.py) |640 |46.4 |12.3 |25.3 |73.8| [Download](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/ERMTP7VFqrVBrXKMU7Vl4TcBQs0SUeCT7kvc-JdIbej4tQ?e=1MDo9y) | |[YOLOX-l](./exps/default/yolox_l.py) |640 |50.0 |14.5 |54.2| 155.6 | [Download](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EWA8w_IEOzBKvuueBqfaZh0BeoG5sVzR-XYbOJO4YlOkRw?e=wHWOBE) | |[YOLOX-x](./exps/default/yolox_x.py) |640 |**51.2** | 17.3 |99.1 |281.9 | [Download](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EdgVPHBziOVBtGAXHfeHI5kBza0q9yyueMGdT0wXZfI1rQ?e=tABO5u) | |[YOLOX-Darknet53](./exps/default/yolov3.py) |640 | 47.4 | 11.1 |63.7 | 185.3 | [Download](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EZ-MV1r_fMFPkPrNjvbJEMoBLOLAnXH-XKEB77w8LhXL6Q?e=mf6wOc) | #### Light Models. |Model |size |mAPval
0.5:0.95 | Params
(M) |FLOPs
(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | |[YOLOX-Nano](./exps/default/nano.py) |416 |25.3 | 0.91 |1.08 | [Download](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EdcREey-krhLtdtSnxolxiUBjWMy6EFdiaO9bdOwZ5ygCQ?e=yQpdds) | |[YOLOX-Tiny](./exps/default/yolox_tiny.py) |416 |31.7 | 5.06 |6.45 | [Download](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EYtjNFPqvZBBrQ-VowLcSr4B6Z5TdTflUsr_gO2CwhC3bQ?e=SBTwXj) | ## Quick Start
Installation 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 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' ```
Demo 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.tar --path assets/dog.jpg --conf 0.3 --nms 0.65 --tsize 640 --save_result ``` or ```shell python tools/demo.py image -f exps/default/yolox_s.py -c /path/to/your/yolox_s.pth.tar --path assets/dog.jpg --conf 0.3 --nms 0.65 --tsize 640 --save_result ``` Demo for video: ```shell python tools/demo.py video -n yolox-s -c /path/to/your/yolox_s.pth.tar --path /path/to/your/video --conf 0.3 --nms 0.65 --tsize 640 --save_result ```
Reproduce our results on COCO Step1. Prepare COCO dataset ```shell cd 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 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 ```
Evaluation We support batch testing for fast evaluation: ```shell python tools/eval.py -n yolox-s -c yolox_s.pth.tar -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.tar -b 1 -d 1 --conf 0.001 --fp16 --fuse yolox-m yolox-l yolox-x ```
Toturials * [Training on custom data](docs/train_custom_data.md).
## Deployment 1. [ONNX export and an ONNXRuntime](./demo/ONNXRuntime) 2. [TensorRT in C++ and Python](./demo/TensorRT) 3. [ncnn in C++ and Java](./demo/ncnn) 4. [OpenVINO in C++ and Python](./demo/OpenVINO) ## Cite YOLOX If you use YOLOX in your research, please cite our work by using the following BibTeX entry: ```latex @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```