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><b>数据集概览</b>MEDISEG（Medication Image SEGmentation，药物图像分割数据集）是一款高质量真实世界数据集，专为药片识别模型的开发与评估打造。它包含两个子集：MEDISEG（3-药片）：受控数据集，包含三种形状与色彩差异细微的药片类型。MEDISEG（32-药片）：多样性更强的数据集，涵盖32种不同的药片类别，可反映真实场景中的各类挑战，例如遮挡、多变光照条件以及单帧内存在多种药物的情况。每个子集均附带COCO格式标注（Common Objects in Context），包含实例分割掩码（instance segmentation masks）、边界框（bounding boxes）与类别标签（class labels）。<b>数据集结构</b>本数据集的组织形式如下：<br><br>MEDISEG/│── LICENSE<br>│── metadata.csv<br>│── 3pills/<br>│ ├── annotations.json<br>│ ├── images/<br>│ │ ├── image1.jpg<br>│ │ ├── image2.jpg<br>│── 32pills/<br>│ ├── annotations.json<br>│ ├── images/<br>│ │ ├── image1.jpg<br>│ │ ├── image2.jpg<br><br>LICENSE: 本数据集采用CC BY 4.0协议进行分发。<br>metadata.csv: 补充性药品信息，涵盖注册编号、商品名、活性成分、监管分类以及官方网址。<br>annotations.json: COCO格式标注文件，提供实例分割掩码、边界框与类别标签。<br>images/: 高分辨率JPG格式的药品图像。<br><b>致谢</b>若使用本数据集，请引用以下相关文献：<br>bibtex<br>@article{MEDISEG2026,<br>title = {用于防范药物不良事件的带实例分割掩码的药品图像数据集},<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}}