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 git@github.com: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
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
- Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo:
- 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 MNN/TNN/ONNXRuntime: YOLOX-MNN、YOLOX-TNN and YOLOX-ONNXRuntime C++ 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}
}
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