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https://github.com/carnozhao/yolox

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

https://github.com/carnozhao/yolox

Science Score: 10.0%

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    Links to: arxiv.org
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    Low similarity (9.5%) to scientific vocabulary
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YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Basic Info
  • Host: GitHub
  • Owner: CarnoZhao
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 5.81 MB
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  • Watchers: 1
  • Forks: 1
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Fork of Megvii-BaseDetection/YOLOX
Created over 4 years ago · Last pushed over 4 years ago
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README.md

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.

This repo is an implementation of PyTorch version YOLOX, there is also a MegEngine implementation.

Updates!!

  • 【2021/08/19】 We optimize the training process with 2x faster training and ~1% higher performance! See notes for more details.
  • 【2021/08/05】 We release MegEngine version YOLOX.
  • 【2021/07/28】 We fix the fatal error of memory leak
  • 【2021/07/26】 We now support MegEngine deployment.
  • 【2021/07/20】 We have released our technical report on Arxiv.

Comming soon

  • [ ] YOLOX-P6 and larger model.
  • [ ] Objects365 pretrain.
  • [ ] Transformer modules.
  • [ ] More features in need.

Benchmark

Standard Models.

|Model |size |mAPval
0.5:0.95 |mAPtest
0.5:0.95 | Speed V100
(ms) | Params
(M) |FLOPs
(G)| weights | | ------ |:---: | :---: | :---: |:---: |:---: | :---: | :----: | |YOLOX-s |640 |40.5 |40.5 |9.8 |9.0 | 26.8 | github | |YOLOX-m |640 |46.9 |47.2 |12.3 |25.3 |73.8| github | |YOLOX-l |640 |49.7 |50.1 |14.5 |54.2| 155.6 | github | |YOLOX-x |640 |51.1 |51.5 | 17.3 |99.1 |281.9 | github | |YOLOX-Darknet53 |640 | 47.7 | 48.0 | 11.1 |63.7 | 185.3 | github |

Legacy 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 | [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 |mAPval
0.5:0.95 | Params
(M) |FLOPs
(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | |YOLOX-Nano |416 |25.8 | 0.91 |1.08 | github | |YOLOX-Tiny |416 |32.8 | 5.06 |6.45 | github |

Legacy 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 | [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 | [github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_tiny_32dot8.pth) |

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 [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 --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] ```
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 -m yolox.tools.train -n yolox-s -d 8 -b 64 --fp16 -o [--cache] 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 * --cache: caching imgs into RAM to accelarate training, which need large system RAM. When using -f, the above commands are equivalent to: ```shell python -m yolox.tools.train -f exps/default/yolox_s.py -d 8 -b 64 --fp16 -o [--cache] exps/default/yolox_m.py exps/default/yolox_l.py exps/default/yolox_x.py ``` **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 Suppose you want to train YOLOX on 2 machines, and your master machines's IP is 123.123.123.123, use port 12312 and TCP. On master machine, run ```shell python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num-machines 2 --machine-rank 0 ``` On the second machine, run ```shell python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num-machines 2 --machine-rank 1 ``` **Others** See more information with the following command: ```shell python -m yolox.tools.train --help ```
Evaluation We support batch testing for fast evaluation: ```shell python -m yolox.tools.eval -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 -m yolox.tools.eval -n yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001 --fp16 --fuse yolox-m yolox-l yolox-x ```
Tutorials * [Training on custom data](docs/train_custom_data.md) * [Manipulating training image size](docs/manipulate_training_image_size.md)

Deployment

  1. MegEngine in C++ and Python
  2. ONNX export and an ONNXRuntime
  3. TensorRT in C++ and Python
  4. ncnn in C++ and Java
  5. OpenVINO in C++ and Python

Third-party resources

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} }

Owner

  • Name: Carno Zhao
  • Login: CarnoZhao
  • Kind: user
  • Location: China
  • Company: UCAS

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