Megvii-BaseDetection/YOLOX Megvii-BaseDetection/YOLOX

  • Stars
    star
    9,231
  • Rank 3,896 (Top 0.08 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created over 3 years ago
  • Updated 4 months ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
Megvii-BaseDetection/YOLOX
github

Megvii-BaseDetection

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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

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!!

  • 【2023/02/28】 We support assignment visualization tool, see doc here.
  • 【2022/04/14】 We support 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

Logging to Weights & Biases

To log metrics, predictions and model checkpoints to W&B use the command line argument --logger wandb and use the prefix "wandb-" to specify arguments for initializing the wandb run.

python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o [--cache] --logger wandb wandb-project <project name>
                         yolox-m
                         yolox-l
                         yolox-x

An example wandb dashboard is available here

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
  6. Accelerate YOLOX inference with nebullvm in 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}
}

In memory of Dr. Jian Sun

Without the guidance of Dr. Jian Sun, YOLOX would not have been released and open sourced to the community. The passing away of Dr. Jian is a huge loss to the Computer Vision field. We add this section here to express our remembrance and condolences to our captain Dr. Jian. It is hoped that every AI practitioner in the world will stick to the concept of "continuous innovation to expand cognitive boundaries, and extraordinary technology to achieve product value" and move forward all the way.

没有孙剑博士的指导，YOLOX也不会问世并开源给社区使用。 孙剑博士的离去是CV领域的一大损失，我们在此特别添加了这个部分来表达对我们的“船长”孙老师的纪念和哀思。 希望世界上的每个AI从业者秉持着“持续创新拓展认知边界，非凡科技成就产品价值”的观念，一路向前。

More Repositories

1

BEVDepth

Official code for BEVDepth.
Python
709
star
2

cvpods

All-in-one Toolbox for Computer Vision Research.
Python
643
star
3

DeFCN

End-to-End Object Detection with Fully Convolutional Network
Python
494
star
4

BorderDet

BorderDet: Border Feature for Dense Object Detection(ECCV2020 Oral)
Python
431
star
5

DynamicRouting

Learning Dynamic Routing for Semantic Segmentation
Python
378
star
6

BEVStereo

Official code for BEVStereo
Python
253
star
7

OTA

Official implementation of our CVPR2021 paper "OTA: Optimal Transport Assignment for Object Detection" in Pytorch.
Python
241
star
8

AutoAssign

Pytorch implementation of "AutoAssign: Differentiable Label Assignment for Dense Object Detection"
Python
140
star
9

TreeFilter-Torch

Learnable Tree Filter for Structure-preserving Feature Transform
Python
139
star
10

DenseTeacher

DenseTeacher: Dense Pseudo-Label for Semi-supervised Object Detection
Python
120
star
11

DisAlign

Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)
Python
117
star
12

Megvii-BaseDetection

You are welcomed to join us!
50
star
13

GFSD

This project provides an implementation for "Generalized Few-Shot Object Detection without Forgetting" (CVPR2021) on PyTorch.
Python
45
star
14

LLA

Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.
Python
35
star
15

4K-Face

4K-Face: A Dataset with Huge Scale-variance Faces
32
star
16

storage

provide Checkpoint for users.
1
star