<strong>Data available for "Identification of herbarium specimen sheet components from high-resolution images using deep learning": YOLOv5 Best model weights for MELU trained object detection model</strong>

Data Available for the paper: "<strong>Identification of herbarium specimen sheet components from high-resolution images using deep learning</strong>", by Karen M Thompson, Robert Turnbull, Emily Fitzgerald, Joanne L Birch <br> This is the 'best' weights for use in a YOLOv5 object detection model. <br> Other information available to support this paper: (1) annotations for selected MELU specimen sheet digital images (2) annotations for benchmark dataset (noting these are specific to the MELU trained model)

本数据集配套发表于论文《基于深度学习的高分辨率植物标本页组分识别》（Identification of herbarium specimen sheet components from high-resolution images using deep learning），作者为Karen M Thompson、Robert Turnbull、Emily Fitzgerald及Joanne L Birch。该数据集为YOLOv5目标检测模型所用的最优权重文件。本论文可获取的其他补充资料包括：(1) 针对选定MELU标本页数字图像的标注集；(2) 基准数据集的标注集（需注意该标注仅适配经MELU训练得到的模型）

搜集汇总

背景与挑战

背景概述

该数据集是YOLOv5对象检测模型的最佳权重文件，专门用于从高分辨率图像中识别植物标本薄片组件，支持深度学习研究。它属于一篇相关论文的数据部分，提供模型权重以辅助植物标本的自动分析，并附带其他注释数据作为补充。