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

Related tags

Deep Learningyolo-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：
    video

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.

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

手部检测预训练模型

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 )
Owner
Eric.Lee
Eric.Lee
GitHub Repository
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