Yoloxkeypointsegment - An anchor-free version of YOLO, with a simpler design but better performance

Related tags

Deep Learningyolox_keypoint_segment
Overview

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
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}
}
Owner
GitHub Repository
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model Baris Gecer 1, Binod Bhattarai 1

Baris Gecer 190 Dec 29, 2022
Lane follower: Lane-detector (OpenCV) + Object-detector (YOLO5) + CAN-bus

Lane Follower This code is for the lane follower, including perception and control, as shown below. Environment Hardware Industrial Camera Intel-NUC(1

Siqi Fan 3 Jul 07, 2022
Official repository accompanying a CVPR 2022 paper EMOCA: Emotion Driven Monocular Face Capture And Animation. EMOCA takes a single image of a face as input and produces a 3D reconstruction. EMOCA sets the new standard on reconstructing highly emotional images in-the-wild

EMOCA: Emotion Driven Monocular Face Capture and Animation Radek Daněček · Michael J. Black · Timo Bolkart CVPR 2022 This repository is the official i

Radek Daněček 339 Dec 30, 2022
Generalized Data Weighting via Class-level Gradient Manipulation

Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas

18 Nov 12, 2022
The repo contains the code of the ACL2020 paper `Dice Loss for Data-imbalanced NLP Tasks`

Dice Loss for NLP Tasks This repository contains code for Dice Loss for Data-imbalanced NLP Tasks at ACL2020. Setup Install Package Dependencies The c

223 Dec 17, 2022
Code & Data for the Paper "Time Masking for Temporal Language Models", WSDM 2022

Time Masking for Temporal Language Models This repository provides a reference implementation of the paper: Time Masking for Temporal Language Models

Guy Rosin 12 Jan 06, 2023
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"

PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)" Unofficial PyTorch Implementation of Progressi

Vitaliy Hramchenko 58 Dec 19, 2022
TransCD: Scene Change Detection via Transformer-based Architecture

TransCD: Scene Change Detection via Transformer-based Architecture

wangzhixue 29 Dec 11, 2022
A Moonraker plug-in for real-time compensation of frame thermal expansion

Frame Expansion Compensation A Moonraker plug-in for real-time compensation of frame thermal expansion. Installation Credit to protoloft, from whom I

58 Jan 02, 2023
Using deep learning model to detect breast cancer.

Breast-Cancer-Detection Breast cancer is the most frequent cancer among women, with around one in every 19 women at risk. The number of cases of breas

1 Feb 13, 2022
PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning"

PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".

Berivan Isik 8 Dec 08, 2022
50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program

50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

komal_lamba 22 Dec 09, 2022
clustimage is a python package for unsupervised clustering of images.

clustimage The aim of clustimage is to detect natural groups or clusters of images. Image recognition is a computer vision task for identifying and ve

Erdogan Taskesen 52 Jan 02, 2023
[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

GCA Source code for Graph Contrastive Learning with Adaptive Augmentation (WWW 2021) For example, to run GCA-Degree under WikiCS, execute: python trai

Big Data and Multi-modal Computing Group, CRIPAC 97 Jan 07, 2023
A really easy-to-use and powerful sudoku solver.

SodukuSolver This is a really useful sudoku solver with a Qt gui. USAGE Enter the numbers in and click "RUN"! If you don't want to wait, simply press

Ujhhgtg Teams 11 Jun 02, 2022
Out-of-Town Recommendation with Travel Intention Modeling (AAAI2021)

TrainOR_AAAI21 This is the official implementation of our AAAI'21 paper: Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, Hui Xiong

Jack Xin 13 Oct 19, 2022
Method for facial emotion recognition compitition of Xunfei and Datawhale .

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档 队名：W03KFgNOc 排名：3 正确率: 0.75564 队员：yyMoming,xkwang,RichardoMu。 比赛链接：人脸情绪识别挑战赛 文章地址:link emotion 该项目分别训练八个模型并生成csv文

6 Oct 17, 2022
Tianshou - An elegant PyTorch deep reinforcement learning library.

Tianshou (天授) is a reinforcement learning platform based on pure PyTorch. Unlike existing reinforcement learning libraries, which are mainly based on

Tsinghua Machine Learning Group 5.5k Jan 05, 2023
Official pytorch implementation of "Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization" ACMMM 2021 (Oral)

Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization This is an official implementation of "Feature Stylization and Domain-

22 Sep 22, 2022
Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

Min-Max Adversarial Attacks [Paper] [arXiv] [Video] [Slide] Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zha

Jingkang Wang 12 Nov 23, 2022