a Pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

Overview

A pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

1. Notes

This is a pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021" [https://arxiv.org/abs/2107.08430]
The repo is still under development

2. Environment

pytorch>=1.7.0, python>=3.6, Ubuntu/Windows, see more in 'requirements.txt'

cd /path/to/your/work
git clone https://github.com/zhangming8/yolox-pytorch.git
cd yolox-pytorch
download pre-train weights in Model Zoo to /path/to/your/work/weights

3. Object Detection

Model Zoo

All weights can be downloaded from GoogleDrive or BaiduDrive (code:bc72)

Model test size mAPval
0.5:0.95
mAPtest
0.5:0.95
Params
(M)
yolox-nano 416 25.4 25.7 0.91
yolox-tiny 416 33.1 33.2 5.06
yolox-s 640 39.3 39.6 9.0
yolox-m 640 46.2 46.4 25.3
yolox-l 640 49.5 50.0 54.2
yolox-x 640 50.5 51.1 99.1
yolox-x 800 51.2 51.9 99.1

mAP was reevaluated on COCO val2017 and test2017, and some results are slightly better than the official implement YOLOX. You can reproduce them by scripts in 'evaluate.sh'

Dataset

download COCO:
http://images.cocodataset.org/zips/train2017.zip
http://images.cocodataset.org/zips/val2017.zip
http://images.cocodataset.org/annotations/annotations_trainval2017.zip

unzip and put COCO dataset in following folders:
/path/to/dataset/annotations/instances_train2017.json
/path/to/dataset/annotations/instances_val2017.json
/path/to/dataset/images/train2017/*.jpg
/path/to/dataset/images/val2017/*.jpg

change opt.dataset_path = "/path/to/dataset" in 'config.py'

Train

See more example in 'train.sh'
a. Train from scratch:(backbone="CSPDarknet-s" means using yolox-s, and you can change it, eg: CSPDarknet-nano, tiny, s, m, l, x)
python train.py gpus='0' backbone="CSPDarknet-s" num_epochs=300 exp_id="coco_CSPDarknet-s_640x640" use_amp=True val_intervals=2 data_num_workers=6 batch_size=48

b. Finetune, download pre-trained weight on COCO and finetune on customer dataset:
python train.py gpus='0' backbone="CSPDarknet-s" num_epochs=300 exp_id="coco_CSPDarknet-s_640x640" use_amp=True val_intervals=2 data_num_workers=6 batch_size=48 load_model="../weights/yolox-s.pth"

c. Resume, you can use 'resume=True' when your training is accidentally stopped:
python train.py gpus='0' backbone="CSPDarknet-s" num_epochs=300 exp_id="coco_CSPDarknet-s_640x640" use_amp=True val_intervals=2 data_num_workers=6 batch_size=48 load_model="exp/coco_CSPDarknet-s_640x640/model_last.pth" resume=True

Some Tips:

a. You can also change params in 'train.sh'(these params will replace opt.xxx in config.py) and use 'nohup sh train.sh &' to train
b. Multi-gpu train: set opt.gpus = "3,5,6,7" in 'config.py' or set gpus="3,5,6,7" in 'train.sh'
c. If you want to close multi-size training, change opt.random_size = None in 'config.py' or set random_size=None in 'train.sh'
d. random_size = (14, 26) means: Randomly select an integer from interval (14,26) and multiply by 32 as the input size
e. Visualized log by tensorboard: 
    tensorboard --logdir exp/your_exp_id/logs_2021-08-xx-xx-xx and visit http://localhost:6006
   Your can also use the following shell scripts:
    (1) grep 'train epoch' exp/your_exp_id/logs_2021-08-xx-xx-xx/log.txt
    (2) grep 'val epoch' exp/your_exp_id/logs_2021-08-xx-xx-xx/log.txt

Evaluate

Module weights will be saved in './exp/your_exp_id/model_xx.pth'
change 'load_model'='weight/path/to/evaluate.pth' and backbone='backbone-type' in 'evaluate.sh'
sh evaluate.sh

Predict/Inference/Demo

a. Predict images, change img_dir and load_model
python predict.py gpus='0' backbone="CSPDarknet-s" vis_thresh=0.3 load_model="exp/coco_CSPDarknet-s_640x640/model_best.pth" img_dir='/path/to/dataset/images/val2017'

b. Predict video
python predict.py gpus='0' backbone="CSPDarknet-s" vis_thresh=0.3 load_model="exp/coco_CSPDarknet-s_640x640/model_best.pth" video_dir='/path/to/your/video.mp4'

You can also change params in 'predict.sh', and use 'sh predict.sh'

Train Customer Dataset(VOC format)

1. put your annotations(.xml) and images(.jpg) into:
    /path/to/voc_data/images/train2017/*.jpg  # train images
    /path/to/voc_data/images/train2017/*.xml  # train xml annotations
    /path/to/voc_data/images/val2017/*.jpg  # val images
    /path/to/voc_data/images/val2017/*.xml  # val xml annotations

2. change opt.label_name = ['your', 'dataset', 'label'] in 'config.py'
   change opt.dataset_path = '/path/to/voc_data' in 'config.py'

3. python tools/voc_to_coco.py
   Converted COCO format annotation will be saved into:
    /path/to/voc_data/annotations/instances_train2017.json
    /path/to/voc_data/annotations/instances_val2017.json

4. (Optional) you can visualize the converted annotations by:
    python tools/show_coco_anns.py
    Here is an analysis of the COCO annotation https://blog.csdn.net/u010397980/article/details/90341223?spm=1001.2014.3001.5501

5. run train.sh, evaluate.sh, predict.sh (are the same as COCO)

4. Multi/One-class Multi-object Tracking(MOT)

one-class/single-class MOT Dataset

DOING

Multi-class MOT Dataset

DOING

Train

DOING

Evaluate

DOING

Predict/Inference/Demo

DOING

5. Acknowledgement

https://github.com/Megvii-BaseDetection/YOLOX
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/open-mmlab/mmdetection
https://github.com/xingyizhou/CenterNet
Self Governing Neural Networks (SGNN): the Projection Layer

Self Governing Neural Networks (SGNN): the Projection Layer A SGNN's word projections preprocessing pipeline in scikit-learn In this notebook, we'll u

Guillaume Chevalier 22 Nov 06, 2022
[CVPR 2022] Structured Sparse R-CNN for Direct Scene Graph Generation

Structured Sparse R-CNN for Direct Scene Graph Generation Our paper Structured Sparse R-CNN for Direct Scene Graph Generation has been accepted by CVP

Multimedia Computing Group, Nanjing University 44 Dec 23, 2022
Massively parallel Monte Carlo diffusion MR simulator written in Python.

Disimpy Disimpy is a Python package for generating simulated diffusion-weighted MR signals that can be useful in the development and validation of dat

Leevi 16 Nov 11, 2022
A simplistic and efficient pure-python neural network library from Phys Whiz with CPU and GPU support.

A simplistic and efficient pure-python neural network library from Phys Whiz with CPU and GPU support.

Manas Sharma 19 Feb 28, 2022
A Deep Learning based project for creating line art portraits.

ArtLine The main aim of the project is to create amazing line art portraits. Sounds Intresting,let's get to the pictures!! Model-(Smooth) Model-(Quali

Vijish Madhavan 3.3k Jan 07, 2023
A public available dataset for road boundary detection in aerial images

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images

Zhenhua Xu 79 Jan 04, 2023
Python Environment for Bayesian Learning

Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl in

Abhik Shah 103 Jul 14, 2022
Finetune alexnet with tensorflow - Code for finetuning AlexNet in TensorFlow >= 1.2rc0

Finetune AlexNet with Tensorflow Update 15.06.2016 I revised the entire code base to work with the new input pipeline coming with TensorFlow = versio

Frederik Kratzert 766 Jan 04, 2023
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

[ICCV2021] TransReID: Transformer-based Object Re-Identification [pdf] The official repository for TransReID: Transformer-based Object Re-Identificati

DamoCV 569 Dec 30, 2022
Official implementation of NPMs: Neural Parametric Models for 3D Deformable Shapes - ICCV 2021

NPMs: Neural Parametric Models Project Page | Paper | ArXiv | Video NPMs: Neural Parametric Models for 3D Deformable Shapes Pablo Palafox, Aljaz Bozic

PabloPalafox 109 Nov 22, 2022
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

Natural Posterior Network This repository provides the official implementation o

Oliver Borchert 54 Dec 06, 2022
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei

Zhuo Zheng 125 Dec 13, 2022
Stochastic Extragradient: General Analysis and Improved Rates

Stochastic Extragradient: General Analysis and Improved Rates This repository is the official implementation of the paper "Stochastic Extragradient: G

Hugo Berard 4 Nov 11, 2022
ParmeSan: Sanitizer-guided Greybox Fuzzing

ParmeSan: Sanitizer-guided Greybox Fuzzing ParmeSan is a sanitizer-guided greybox fuzzer based on Angora. Published Work USENIX Security 2020: ParmeSa

VUSec 158 Dec 31, 2022
Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)

Certified Robustness to Adversarial Word Substitutions This is the official GitHub repository for the following paper: Certified Robustness to Adversa

Robin Jia 38 Oct 16, 2022
Docker containers of baseline agents for the Crafter environment

Crafter Baselines This repository contains Docker containers for running various baselines on the Crafter environment. Reward Agents DreamerV2 based o

Danijar Hafner 17 Sep 25, 2022
IA for recognising Traffic Signs using Keras [Tensorflow]

Traffic Signs Recognition ⚠️ 🚦 Fundamentals of Intelligent Systems Introduction 📄 Development of a neural network capable of recognizing nine differ

Sebastián Fernández García 2 Dec 19, 2022
Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)

Fast and Flexible Temporal Point Processes with Triangular Maps This repository includes a reference implementation of the algorithms described in "Fa

Oleksandr Shchur 20 Dec 02, 2022
Code for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning Pytorch Implementation for DisCo: Remedy Self-supervi

79 Jan 06, 2023
Snscrape-jsonl-urls-extractor - Extracts urls from jsonl produced by snscrape

snscrape-jsonl-urls-extractor extracts urls from jsonl produced by snscrape Usag

1 Feb 26, 2022