YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

Related tags

Deep Learning YOLOX-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！
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