书接上文，基于藏文手写数字数据开发构建yolov5n轻量级藏文手写数字检测识别系统

news2024/11/28 12:52:32

在上一篇文章中：
《python基于轻量级CNN模型开发构建手写藏文数字识别系统》

开发实现了轻量级的藏文手写数字识别系统，这里主要是想基于前文的数据，整合目标检测模型来进一步挖掘藏文手写数字数据集的可玩性，基于yolov5n开发构建轻量级的藏文手写数字检测识别系统，首先来看效果图：

共仿真生成了3000的样本数据集，接下来简单看下：

YOLO格式标注文件如下所示：

实例标注内容如下：

VOC格式标注文件如下所示：

实例标注内容如下所示：

<annotation>
    <folder>JiaGuWen</folder>
    <filename>JPEGImages/0a48304e-c797-4686-9c2a-09eeb029404d.jpg</filename>
    <source>
        <database>The JiaGuWen Database</database>
        <annotation>JiaGuWen</annotation>
        <image>JiaGuWen</image>
    </source>
    <owner>
        <name>CGB</name>
    </owner>    
    <size>
        <width>640</width>
        <height>640</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    
    <object>        
        <name>0</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>590</xmin>
            <ymin>14</ymin>
            <xmax>618</xmax>
            <ymax>42</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>7</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>392</xmin>
            <ymin>98</ymin>
            <xmax>448</xmax>
            <ymax>154</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>1</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>145</xmin>
            <ymin>134</ymin>
            <xmax>187</xmax>
            <ymax>176</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>1</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>380</xmin>
            <ymin>352</ymin>
            <xmax>408</xmax>
            <ymax>380</ymax>
        </bndbox>
    </object>
    
</annotation>

模型文件如下：

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 10  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

训练数据配置文件如下：