<strong>Data available for "Identification of herbarium specimen sheet components from high-resolution images using deep learning": Annotations for selected MELU specimen sheet digital images</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> 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/ <br> These annotations for use in a YOLO object detection model. <br> 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<br> 2 stamp<br> 3 annotation label<br> 4 scale<br> 5 swing tag<br> 6 full database label<br> 7 database label<br> 8 swatch<br> 9 institutional label<br> 10 number (ii) then the following four elements are the corner coordinates for the bounding box <br> 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)