图像识别训练-气泡识别-YOLO-气泡识别数据集
内容包含1000张气泡图像和对应的YOLO标注txt文件，在机器学习和计算机视觉领域，YOLO（You Only Look Once）是一种流行的实时对象检测系统，它能够在单个前向传播中同时预测对象的边界框和类别概率。当处理包含气泡图像的数据集时，使用YOLO进行标注和训练可以实现对气泡的自动检测和定位。YOLO（You Only Look Once）是一种流行的实时目标检测算法，由美国研究人员约瑟夫·雷德蒙德·斯塔克（Joseph Redmon）在2016年提出。YOLO算法的主要特点是将目标检测任务转化为单个神经网络的回归问题，从而实现了高效的实时目标检测。YOLO算法的主要思想是将输入图像划分为S×S个网格单元，每个网格单元负责预测B个边界框（Bounding Box）以及这些边界框的置信度和类别。具体来说，每个边界框包含5个预测值，分别为边界框的中心坐标（x, y）、边界框的宽度和高度（w, h），以及一个置信度（c），置信度表示边界框内存在目标的可能性以及边界框与真实目标框的重合度（IOU，Intersection Over Union）。
在YOLO中，每个网格单元只负责
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