Introduction
- 关键点版本：已完成
- 全景分割版本：已完成
- 实例分割版本：已完成
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 | 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.
git clone [email protected]: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.
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:
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 tools/train.py -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 tools/train.py -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
Evaluation
We support batch testing for fast evaluation:
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:
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
Deployment
- MegEngine in C++ and Python
- ONNX export and an ONNXRuntime
- TensorRT in C++ and Python
- ncnn in C++ and Java
- OpenVINO in C++ and Python
Third-party resources
- The ncnn android app with video support: ncnn-android-yolox from FeiGeChuanShu
- YOLOX with Tengine support: Tengine from BUG1989
- YOLOX + ROS2 Foxy: YOLOX-ROS from Ar-Ray
- YOLOX Deploy DeepStream: YOLOX-deepstream from nanmi
- YOLOX ONNXRuntime C++ Demo: lite.ai from DefTruth
- Converting darknet or yolov5 datasets to COCO format for YOLOX: YOLO2COCO from Daniel
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}
}
49 Jul 27, 2022
3k Dec 26, 2022
69 Nov 28, 2022
70 Dec 04, 2022
244 Jan 01, 2023
14 Oct 18, 2022
19 Mar 29, 2022
992 Jan 04, 2023
2 Dec 23, 2021
68 Dec 29, 2022
104 Nov 16, 2022
4 May 20, 2022
112 Nov 28, 2022
706 Dec 30, 2022
17 Dec 05, 2022
50 Dec 05, 2022
514 Dec 28, 2022
117 Dec 27, 2022
8 Nov 20, 2022
4 Dec 20, 2022