YOLOv5水果缺陷检测数据集：11类完整标注

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资源摘要信息:"该数据集是专为使用YOLOv5框架进行目标检测而设计的，包含11个不同类别的水果图像，涵盖了水果的质量与缺陷检测。具体类别包括好的苹果、腐烂的苹果、好的香蕉、腐烂的香蕉等，总计11类。数据集已按照YOLOv5格式进行了整理，每个图像都对应一个标注文件，标注文件中包含了目标的边界框信息。数据集图像分辨率为640*640的RGB格式，具有清晰的边界框标注和完整的图像信息。数据集分为训练集和验证集两部分，分别包含1811张图片和1811个标签txt文件，以及514张图片和514个标签txt文件。整个数据集压缩后的大小为103 MB。除此之外，还提供了一个可视化脚本文件，该文件可以加载任何一张图像并绘制出其对应的边界框，并将绘制结果保存在当前目录下。由于脚本已经编写完成且无需修改，因此可以直接运行以查看检测结果。在数据集的使用上，用户无需进行额外的数据处理，即可直接应用于训练和验证YOLOv5模型。由于YOLOv5本身是一个流行且高效的实时目标检测系统，因此该数据集能够为研究者和开发者提供一种快速且有效的方法来训练和评估模型在水果好坏缺陷检测任务上的性能。"

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yolov5数据集：多种水果好坏缺陷检测（11类别，包含训练集、验证集）（2000个子文件）

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show.py 2KB

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共 2000 条

Ai医学图像分割

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资源: 2092

YOLOv5水果缺陷检测数据集：11类完整标注

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