YOLOv5目标检测数据集：橘子成熟度分类与可视化

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资源摘要信息:"本资源是一个专门针对柑橘成熟度检测的数据集，用于训练和验证目标检测模型。数据集包含了两种类别：成熟和未成熟的橘子。它遵循YOLOv5的文件夹结构，方便直接用于训练和验证过程，无需额外的数据预处理。数据集的图像分辨率为640*640的RGB格式，图像清晰，包含边界框标注，适合作为机器学习和深度学习训练的输入数据。数据集被分为训练集和验证集两部分，分别由2313张和224张图像及相应的标注文件组成。每个图像文件都附带一个文本文件，其中包含相应的标注信息，用于在YOLOv5框架中进行目标检测。数据集的压缩后总大小为79MB，解压后可用于支持各种视觉任务，例如在自动化采摘系统中，通过识别橘子的成熟程度来指导采摘机器人的行为。此外，为了方便用户查看数据集的效果，资源中还包含了一个可视化脚本，该脚本能够接收任意一张图像作为输入，并在图像上绘制出相应的边界框，进而更直观地展示数据集的效果。整个资源的标签包括数据集、检测、柑橘和成熟度检测等关键词，能够准确反映出本资源的主要内容和用途。文件名称列表中，资源被命名为"橘子成熟度检测数据"，明确指出了数据集的主题和目的。综上所述，本资源是一个专为成熟度检测设计的柑橘数据集，适用于YOLOv5目标检测模型的训练和验证，同时提供了一个便捷的工具来进行数据可视化，使得研究者和开发者可以更容易地对数据集进行理解和应用。"

收起资源包目录

yolov5数据集：橘子是否成熟检测（2类别，包含训练集、验证集）（2000个子文件）

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共 2000 条

Ai医学图像分割

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