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All Projects → Megvii-BaseDetection → YOLOX

Megvii-BaseDetection / YOLOX

Licence: Apache-2.0 license
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

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deep-learningpytorchyoloobject-detectiontensorrtncnnonnxyolov3openvinomegengineyolox

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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!!

  • 【2022/04/14】 We suport jit compile op.
  • 【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.

Coming 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 640 39.6 9.8 9.0 26.8 onedrive/github
YOLOX-m 640 46.4 12.3 25.3 73.8 onedrive/github
YOLOX-l 640 50.0 14.5 54.2 155.6 onedrive/github
YOLOX-x 640 51.2 17.3 99.1 281.9 onedrive/github
YOLOX-Darknet53 640 47.4 11.1 63.7 185.3 onedrive/github

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 416 25.3 0.91 1.08 github
YOLOX-Tiny 416 32.8 5.06 6.45 github

Quick Start

Installation

Step1. Install YOLOX from source.

git clone [email protected]:Megvii-BaseDetection/YOLOX.git
cd YOLOX
pip3 install -v -e .  # or  python3 setup.py develop
Demo

Step1. Download a pretrained model from the benchmark table.

Step2. Use either -n or -f to specify your detector's config. For example:

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

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:

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

cd <YOLOX_HOME>
ln -s /path/to/your/COCO ./datasets/COCO

Step2. Reproduce our results on COCO by specifying -n:

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:

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

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

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:

python -m yolox.tools.train --help
Evaluation

We support batch testing for fast evaluation:

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:

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

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:

 @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}
}
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