MEDISEG

<b>Dataset Overview</b>MEDISEG (MEDication Image SEGmentation) is a high-quality, real-world dataset designed for the development and evaluation of pill recognition models. It contains two subsets:MEDISEG (3-Pills): A controlled dataset featuring three pill types with subtle differences in shape and color.MEDISEG (32-Pills): A more diverse dataset containing 32 distinct pill classes, reflecting real-world challenges such as occlusions, varied lighting conditions, and multiple medications in a single frame.Each subset includes COCO-format annotations with instance segmentation masks, bounding boxes, and class labels.<b>Dataset Structure</b>The dataset is organized as follows:<br><br>MEDISEG/│── LICENSE<br>│── metadata.csv│── 3pills/<br>│ ├── annotations.json│ ├── images/<br>│ │ ├── image1.jpg│ │ ├── image2.jpg<br>│── 32pills/│ ├── annotations.json<br>│ ├── images/│ │ ├── image1.jpg<br>│ │ ├── image2.jpg<br>LICENSE: The CC BY 4.0 license under which the dataset is distributed.metadata.csv: Supplementary drug information, including registration numbers, brand names, active ingredients, regulatory classifications, and official URLs.annotations.json: COCO-format annotation files providing segmentation masks, bounding boxes, and class labels.images/: High-resolution JPG images of medications.<b>Acknowledgements</b>If you use this dataset, please cite the corresponding publication:<br>bibtex<br>@article{MEDISEG2026,<br>title = {A dataset of medication images with instance segmentation masks for preventing adverse drug events},<br>author = {Chu, Wai Ip and Hirani, Shashi and Tarroni, Giacomo and Li, Ling},<br>journal={arXiv preprint arXiv:2603.10825},<br>year = {2026},<br>doi={10.48550/arXiv.2603.10825}}<br>