YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

Related tags

Deep LearningYOLOX-Paddle
Overview

YOLOX-Paddle

A reproduction of YOLOX by PaddlePaddle

数据集准备

下载COCO数据集，准备为如下路径

/home/aistudio
|-- COCO
|   |-- annotions
|   |-- train2017
|   |-- val2017

除了常用的图像处理库，需要安装额外的包

pip install gputil==1.4.0 loguru pycocotools

进入仓库根目录，编译安装（推荐使用AIStudio）

cd YOLOX-Paddle
pip install -v -e .

如果使用本地机器出现编译失败，需要修改YOLOX-Paddle/yolox/layers/csrc/cocoeval/cocoeval.h中导入pybind11的include文件为本机目录，使用如下命令获取pybind11的include目录

>>> import pybind11
>>> pybind11.get_include()
'/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pybind11/include'

如AIStudio路径

#include </opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pybind11/include/pybind11/numpy.h>
#include </opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pybind11/include/pybind11/pybind11.h>
#include </opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pybind11/include/pybind11/stl.h>
#include </opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pybind11/include/pybind11/stl_bind.h>

成功后使用pip list可看到安装模块

yolox    0.1.0    /home/aistudio/YOLOX-Paddle

设置YOLOX_DATADIR环境变量\或者`ln -s /path/to/your/COCO ./datasets/COCO`来指定COCO数据集位置

export YOLOX_DATADIR=/home/aistudio/

训练

python tools/train.py -n yolox-nano -d 1 -b 64

得到的权重保存至./YOLOX_outputs/nano/yolox_nano.pdparams

验证

python tools/eval.py -n yolox-nano -c ./YOLOX_outputs/nano/yolox_nano.pdparams -b 64 -d 1 --conf 0.001

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.259
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.416
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.269
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.242
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.384
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.419
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.470
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.632

并提供了官方预训练权重，code:ybxc

Model size mAPval
0.5:0.95		 mAPtest
0.5:0.95		 Speed V100
(ms)		 Params
(M)		 FLOPs
(G)
YOLOX-s 640 40.5 40.5 9.8 9.0 26.8
YOLOX-m 640 46.9 47.2 12.3 25.3 73.8
YOLOX-l 640 49.7 50.1 14.5 54.2 155.6
YOLOX-x 640 51.1 51.5 17.3 99.1 281.9
YOLOX-Darknet53 640 47.7 48.0 11.1 63.7 185.3

推理

python tools/demo.py image -n yolox-nano -c ./YOLOX_outputs/nano/yolox_nano.pdparams --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result

推理结果如下所示

Train Custom Data

相信这是大部分开发者最关心的事情，本章节参考如下仓库，本仓库现已集成

  • Converting darknet or yolov5 datasets to COCO format for YOLOX: YOLO2COCO from Daniel

数据准备

我们同样以YOLOv5格式的光栅数据集为例，可在此处下载 进入仓库根目录，下载解压，数据集应该具有如下目录：

YOLOX-Paddle
|-- guangshan
|   |-- images
|      |-- train
|      |-- val
|   |-- labels
|      |-- train
|      |-- val

现在运行如下命令

bash prepare.sh

然后添加一个classes.txt，你应该得到如下目录，并在生成的YOLOV5_COCO_format得到COCO数据格式的数据集：

YOLOX-Paddle/YOLO2COCO/dataset
|-- YOLOV5
|   |-- guangshan
|   |   |-- images
|   |   |-- labels
|   |-- train.txt
|   |-- val.txt
|   |-- classes.txt
|-- YOLOV5_COCO_format
|   |-- train2017
|   |-- val2017
|   |-- annotations

可参考YOLOV5_COCO_format下的README.md

训练、验证、推理

配置custom训练文件YOLOX-Paddle/exps/example/custom/nano.py，修改self.num_classes为你的类别数，其余配置可根据喜好调参，使用如下命令启动训练

python tools/train.py -f ./exps/example/custom/nano.py -n yolox-nano -d 1 -b 8

使用如下命令启动验证

python tools/eval.py -f ./exps/example/custom/nano.py -n yolox-nano -c ./YOLOX_outputs_custom/nano/best_ckpt.pdparams -b 64 -d 1 --conf 0.001

使用如下命令启动推理

python tools/demo.py image -f ./exps/example/custom/nano.py -n yolox-nano -c ./YOLOX_outputs_custom/nano/best_ckpt.pdparams --path test.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result

其余部分参考COCO数据集，整个训练文件保存在YOLOX_outputs_custom文件夹

关于作者

姓名 郭权浩
学校 电子科技大学研2020级
研究方向 计算机视觉
CSDN主页 Deep Hao的CSDN主页
GitHub主页 Deep Hao的GitHub主页
如有错误，请及时留言纠正，非常蟹蟹！
后续会有更多论文复现系列推出，欢迎大家有问题留言交流学习，共同进步成长！
Owner
QuanHao Guo
Master at UESTC
QuanHao Guo
GitHub Repository
MolRep: A Deep Representation Learning Library for Molecular Property Prediction

MolRep: A Deep Representation Learning Library for Molecular Property Prediction Summary MolRep is a Python package for fairly measuring algorithmic p

AI-Health @NSCC-gz 83 Dec 24, 2022
The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

Yuki M. Asano 249 Dec 22, 2022
Implementation of "Semi-supervised Domain Adaptive Structure Learning"

Semi-supervised Domain Adaptive Structure Learning - ASDA This repo contains the source code and dataset for our ASDA paper. Illustration of the propo

3 Dec 13, 2021
Swapping face using Face Mesh with TensorFlow Lite

Swapping face using Face Mesh with TensorFlow Lite

iwatake 17 Apr 26, 2022
Python codes for Lite Audio-Visual Speech Enhancement.

Lite Audio-Visual Speech Enhancement (Interspeech 2020) Introduction This is the PyTorch implementation of Lite Audio-Visual Speech Enhancement (LAVSE

Shang-Yi Chuang 85 Dec 01, 2022
[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

DiffMG This repository contains the code for our KDD 2021 Research Track paper: DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neura

AutoML Research 24 Nov 29, 2022
Table-Extractor 表格抽取

(t)able-(ex)tractor 本项目旨在实现pdf表格抽取。 Models 版面分析模块（Yolo） 表格结构抽取（ResNet + Transformer） 文字识别模块（CRNN + CTC Loss） Acknowledgements TableMaster attention-i

2 Jan 15, 2022
这是一个yolox-pytorch的源码，可以用于训练自己的模型。

YOLOX：You Only Look Once目标检测模型在Pytorch当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤

Bubbliiiing 613 Jan 05, 2023
Source code for the ACL-IJCNLP 2021 paper entitled "T-DNA: Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation" by Shizhe Diao et al.

T-DNA Source code for the ACL-IJCNLP 2021 paper entitled Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adapta

shizhediao 17 Dec 22, 2022
The Official PyTorch Implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 spotlight paper)

Official PyTorch implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 Spotlight Paper) Zhisheng

NVIDIA Research Projects 45 Dec 26, 2022
Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

DTU Acoustic Technology Group 11 Dec 17, 2022
QT Py Media Knob using rotary encoder & neopixel ring

QTPy-Knob QT Py USB Media Knob using rotary encoder & neopixel ring The QTPy-Knob features: Media knob for volume up/down/mute with "qtpy-knob.py" Cir

Tod E. Kurt 56 Dec 30, 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.

Federated Research Federated Research is a collection of research projects related to Federated Learning and Federated Analytics. Federated learning i

Google Research 483 Jan 05, 2023
An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]

Deep-motion-editing This library provides fundamental and advanced functions to work with 3D character animation in deep learning with Pytorch. The co

1.2k Dec 29, 2022
Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

CLIPstyler Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition" Environment Pytorch 1.7.1, Python 3.6 $ c

203 Dec 30, 2022
Tools for investing in Python

InvestOps Original repository on GitHub Original author is Magnus Erik Hvass Pedersen Introduction This is a Python package with simple and effective

24 Nov 26, 2022
Preparation material for Dropbox interviews

Dropbox-Onsite-Interviews A guide for the Dropbox onsite interview! The Dropbox interview question bank is very small. The bank has been in a Chinese

386 Dec 31, 2022
Code, Models and Datasets for OpenViDial Dataset

OpenViDial This repo contains downloading instructions for the OpenViDial dataset in 《OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Vis

119 Dec 08, 2022
The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter

FAPIS The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter Introduction This repo is primari

Khoi Nguyen 8 Dec 11, 2022
Simple PyTorch implementations of Badnets on MNIST and CIFAR10.

Simple PyTorch implementations of Badnets on MNIST and CIFAR10.

Vera 75 Dec 13, 2022