MEDISEG

Dataset OverviewMEDISEG (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.
Dataset StructureThe dataset is organized as follows:

MEDISEG/
│── LICENSE
│── metadata.csv
│── 3pills/
│ ├── annotations.json
│ ├── images/
│ │ ├── image1.jpg
│ │ ├── image2.jpg
│── 32pills/
│ ├── annotations.json
│ ├── images/
│ │ ├── image1.jpg
│ │ ├── image2.jpg

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.AcknowledgementsIf you use this dataset, please cite the corresponding publication:
bibtex
@article{MEDISEG2026,
title = {A dataset of medication images with instance segmentation masks for preventing adverse drug events},
author = {Chu, Wai Ip and Hirani, Shashi and Tarroni, Giacomo and Li, Ling},
journal={arXiv preprint arXiv:2603.10825},
year = {2026},
doi={10.48550/arXiv.2603.10825}
}