<?xml version='1.0' encoding='utf-8'?>
<annotation>
<filename>img_0702_59.jpg</filename>
<size>
<width>416</width>
<height>416</height>
<depth>3</depth>
</size>
<object>
<name>Capacete</name>
<difficult>0</difficult>
<bndbox>
<xmin>391</xmin>
<ymin>47</ymin>
<xmax>416</xmax>
<ymax>101</ymax>
</bndbox>
</object>
<object>
<name>Balaclava</name>
<difficult>0</difficult>
<bndbox>
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<ymin>99</ymin>
<xmax>415</xmax>
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<object>
<name>colete</name>
<difficult>0</difficult>
<bndbox>
<xmin>354</xmin>
<ymin>184</ymin>
<xmax>415</xmax>
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</bndbox>
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<object>
<name>Capacete</name>
<difficult>0</difficult>
<bndbox>
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<ymin>45</ymin>
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<ymax>109</ymax>
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<object>
<name>C_eletrica</name>
<difficult>0</difficult>
<bndbox>
<xmin>118</xmin>
<ymin>167</ymin>
<xmax>219</xmax>
<ymax>416</ymax>
</bndbox>
</object>
<object>
<name>C_eletrica</name>
<difficult>0</difficult>
<bndbox>
<xmin>110</xmin>
<ymin>97</ymin>
<xmax>221</xmax>
<ymax>415</ymax>
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</object>
</annotation>
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yolo算法-工地佩戴头盔数据集-1608张图像带标签-epi-d4clr.zip
共2000个文件
xml:1272个
txt:728个
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yolo系列算法目标检测数据集,包含标签,可以直接训练模型和验证测试,数据集已经划分好,适用yolov5,yolov8,yolov9,yolov7,yolov10,yolo11算法; 包含两种标签格:yolo格式(txt文件)和voc格式(xml文件),分别保存在两个文件夹中; yolo格式:<class> <x_center> <y_center> <width> <height>, 其中: <class> 是目标的类别索引(从0开始)。 <x_center> 和 <y_center> 是目标框中心点的x和y坐标,这些坐标是相对于图像宽度和高度的比例值,范围在0到1之间。 <width> 和 <height> 是目标框的宽度和高度,也是相对于图像宽度和高度的比例值
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yolo算法-工地佩戴头盔数据集-1608张图像带标签-epi-d4clr.zip (2000个子文件)
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