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
deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and different optimization choices

deep_nn_model_with_only_python_100%_test_accuracy deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and differen

0 Aug 28, 2022
Attention-driven Robot Manipulation (ARM) which includes Q-attention

Attention-driven Robotic Manipulation (ARM) This codebase is home to: Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation I

Stephen James 84 Dec 29, 2022
Posterior predictive distributions quantify uncertainties ignored by point estimates.

Posterior predictive distributions quantify uncertainties ignored by point estimates.

DeepMind 177 Dec 06, 2022
HashNeRF-pytorch - Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives

HashNeRF-pytorch Instant-NGP recently introduced a Multi-resolution Hash Encodin

Yash Sanjay Bhalgat 616 Jan 06, 2023
A novel benchmark dataset for Monocular Layout prediction

AutoLay AutoLay: Benchmarking Monocular Layout Estimation Kaustubh Mani, N. Sai Shankar, J. Krishna Murthy, and K. Madhava Krishna Abstract In this pa

Kaustubh Mani 39 Apr 26, 2022
A Simplied Framework of GAN Inversion

Framework of GAN Inversion Introcuction You can implement your own inversion idea using our repo. We offer a full range of tuning settings (in hparams

Kangneng Zhou 13 Sep 27, 2022
An Official Repo of CVPR '20 "MSeg: A Composite Dataset for Multi-Domain Segmentation"

This is the code for the paper: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation (CVPR 2020, Official Repo) [CVPR PDF] [Journal PDF] J

226 Nov 05, 2022
Planning from Pixels in Environments with Combinatorially Hard Search Spaces -- NeurIPS 2021

PPGS: Planning from Pixels in Environments with Combinatorially Hard Search Spaces Environment Setup We recommend pipenv for creating and managing vir

Autonomous Learning Group 11 Jun 26, 2022
RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues

RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues FGBG (foreground-background) pytorch package for defining and training model

Klaas Kelchtermans 1 Jun 02, 2022
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks Contributions A novel pairwise feature LSP to extract structural

31 Dec 06, 2022
A Transformer-Based Siamese Network for Change Detection

ChangeFormer: A Transformer-Based Siamese Network for Change Detection (Under review at IGARSS-2022) Wele Gedara Chaminda Bandara, Vishal M. Patel Her

Wele Gedara Chaminda Bandara 214 Dec 29, 2022
Encode and decode text application

Text Encoder and Decoder Encode and decode text in many ways using this application! Encode in: ASCII85 Base85 Base64 Base32 Base16 Url MD5 Hash SHA-1

Alice 1 Feb 12, 2022
Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Status: Archive (code is provided as-is, no updates expected) InfoGAN Code for reproducing key results in the paper InfoGAN: Interpretable Representat

OpenAI 1k Dec 19, 2022
[arXiv] What-If Motion Prediction for Autonomous Driving ❓🚗💨

WIMP - What If Motion Predictor Reference PyTorch Implementation for What If Motion Prediction [PDF] [Dynamic Visualizations] Setup Requirements The W

William Qi 96 Dec 29, 2022
Deep learning library for solving differential equations and more

DeepXDE Voting on whether we should have a Slack channel for discussion. DeepXDE is a library for scientific machine learning. Use DeepXDE if you need

Lu Lu 1.4k Dec 29, 2022
Code for the paper: Sketch Your Own GAN

Sketch Your Own GAN Project | Paper | Youtube | Slides Our method takes in one or a few hand-drawn sketches and customizes an off-the-shelf GAN to mat

677 Dec 28, 2022
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations

💎A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily instal

DefTruth 142 Dec 25, 2022
LightNet++: Boosted Light-weighted Networks for Real-time Semantic Segmentation

LightNet++ !!!New Repo.!!! ⇒ EfficientNet.PyTorch: Concise, Modular, Human-friendly PyTorch implementation of EfficientNet with Pre-trained Weights !!

linksense 237 Jan 05, 2023
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences 1. Introduction This project is for paper Model-free Vehicle Tracking and St

TuSimple 92 Jan 03, 2023
Lightwood is Legos for Machine Learning.

Lightwood is like Legos for Machine Learning. A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glu

MindsDB Inc 312 Jan 08, 2023