Data available for "Identification of herbarium specimen sheet components from high-resolution images using deep learning": Annotations for selected MELU specimen sheet digital images

Data Available for the paper: "Identification of herbarium specimen sheet components from high-resolution images using deep learning", by Karen M Thompson, Robert Turnbull, Emily Fitzgerald, Joanne L Birch

These are specific annotations of selected specimen sheet digital images from the MELU collection (Melbourne University Herbarium). MELU collection images are available: https://online.herbarium.unimelb.edu.au/ 

These annotations for use in a YOLO object detection model.

The format of this file is a .ZIP containing a .TXT for each image annotated. 
Each .TXT file will have a row for each annotated element.
Eg. "4 0.064133 0.414363 0.072186 0.309392"
(i) first element is an integer identifying the object type:
 0 small database label
 1 handwritten data
2 stamp
3 annotation label
4 scale
5 swing tag
6 full database label
7 database label
8 swatch
9 institutional label
10 number
(ii) then the following four elements are the corner coordinates for the bounding box

Other information available to support this paper:
(1) annotations for benchmark dataset (noting these are specific to the MELU trained model) 
(2) MELU-trained sheet-component object detection model weights (for application in YOLOv5)

数据集描述翻译：

可供论文《利用深度学习从高分辨率图像中识别标本纸组件》使用的数据，该论文由Karen M Thompson、Robert Turnbull、Emily Fitzgerald和Joanne L Birch共同撰写。本数据集包含来自MELU（墨尔本大学标本馆）选定标本纸数字图像的特定标注。MELU标本馆图像可通过以下链接获取：https://online.herbarium.unimelb.edu.au/

这些标注可用于YOLO目标检测模型。

文件格式为包含每个标注图像的对应TXT文件的.ZIP文件。

每个TXT文件将包含每行一个标注元素。
例如："4 0.064133 0.414363 0.072186 0.309392"

（i）第一个元素是一个整数，用于标识对象类型：
0 小型数据库标签
1 手写数据
2 印章
3 标注标签
4 尺度
5 挂签
6 完整数据库标签
7 数据库标签
8 样品
9 机构标签
10 数字
（ii）随后四个元素是边界框的角坐标。

其他支持本论文的信息包括：
（1）基准数据集的标注（注意这些标注特定于MELU训练模型）
（2）MELU训练的标本组件目标检测模型权重（适用于YOLOv5应用）