数据集格式:YOLO关键点格式(注意这个不是目标检测或者分割的YOLO格式,仅仅包含jpg图片以及对应的yolo格式txt文件)
图片数量(jpg文件个数):923
标注数量(txt文件个数):923
训练集数量:823
验证集数量:100
测试集数量:0
标注类别数:1
标注类别名称:['COW']
标注的关键点数:25
关键点名称:["head","nose","eyeL","eyeR","earbaseL","earbaseR","neck","withers","midBack","midHooks","elbowFL","elbowFR","ribL","ribR","hookL","hookR","elbowBL","kneeBL","pawBL","elbowBR","kneeBR","pawBR","pinL","pinR","tailbase"]
每个类别标注的框数:
COW 框数=1585
总框数=1585
图片分辨率:1920×1080
所在github仓库:firc-dataset
重要说明:数据集已经划分好训练验证测试集可以直接用于yolov5-pose或者yolov8-pose或者yolov11-pose或者yolov26-pose训练
特别声明:本数据集不对训练的模型或者权重文件精度作任何保证
图片预览:


标注例子:
原图(随机选16张图):

标注绘制结果:



![YOLO + DeepSeek (LLM) 智慧消防系统 YOLO+deepsseek 火灾检测系统[火灾烟雾识别系统] YOLO加人工智能AI识别大模型 后端采用 FastAPI,前端采用 Vue-171主机测评](https://www.171host.com/wp-content/uploads/2026/08/20260815062302-6a8005c688f0f-220x150.png)

