pytorch, hand(object) detect ,yolo v5，手检测

Eric.Lee

Last update: Dec 20, 2022

Related tags

Deep Learning yolo-v5

Overview

YOLO V5

物体检测，包括手部检测。

项目介绍

手部检测

手部检测示例如下：

视频示例：

项目配置

作者开发环境：
Python 3.7
PyTorch >= 1.5.1

数据集

手部检测数据集

该项目数据集采用 TV-Hand 和 COCO-Hand (COCO-Hand-Big 部分) 进行制作。
TV-Hand 和 COCO-Hand数据集官网地址 http://vision.cs.stonybrook.edu/~supreeth/

感谢数据集贡献者。    
Paper：  
Contextual Attention for Hand Detection in the Wild. S. Narasimhaswamy, Z. Wei, Y. Wang, J. Zhang, and M. Hoai, IEEE International Conference on Computer Vision, ICCV 2019.

该项目制作的训练集的数据集下载地址(百度网盘 Password: 25y3 )

所有数据集的数据格式

size是全图分辨率， (x，y) 是目标物体中心对于全图的归一化坐标，w,h是目标物体边界框对于全图的归一化宽、高。

dw = 1./(size[0])  
dh = 1./(size[1])  
x = (box[0] + box[1])/2.0 - 1  
y = (box[2] + box[3])/2.0 - 1  
w = box[1] - box[0]  
h = box[3] - box[2]  
x = x*dw  
w = w*dw  
y = y*dh  
h = h*dh

为了更好了解标注数据格式，可以通过运行 show_yolo_anno.py 脚本进行制作数据集的格式。注意配置脚本里的path和path_voc_names，path为标注数据集的相关文件路径，path_voc_names为数据集配置文件。

制作自己的训练数据集

如下所示,每一行代表一个物体实例，第一列是标签，后面是归一化的中心坐标(x,y),和归一化的宽(w)和高(h)，且每一列信息空格间隔。归一化公式如上，同时可以通过show_yolo_anno.py进行参数适配后，可视化验证其正确性。

label     x                  y                   w                  h
0 0.6200393316313977 0.5939000244140625 0.17241466452130497 0.14608001708984375
0 0.38552491996544863 0.5855700073242187 0.14937006832733554 0.1258599853515625
0 0.32889763138738515 0.701989990234375 0.031338589085055775 0.0671400146484375
0 0.760577424617577 0.69422998046875 0.028556443261975064 0.0548599853515625
0 0.5107086662232406 0.6921500244140625 0.018792660530470802 0.04682000732421875
0 0.9295538153861138 0.67602001953125 0.03884511231750328 0.01844000244140625

预训练模型

从零开始预训练模型

预训练模型下载地址(百度网盘 Password: ad4l )

手部检测预训练模型

包括yolo_v5预训练模型图像输入尺寸640。
预训练模型下载地址(百度网盘 Password: x7d4 )

项目使用方法

数据集可视化

根目录下运行命令： show_yolo_anno.py (注意脚本内相关参数配置 )

模型训练

根目录下运行命令： python train.py (注意脚本内相关参数配置 )

模型推理

根目录下运行命令： python video.py (注意脚本内相关参数配置 )

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