ePillID Benchmark 
This repository contains data and code for ePillID - a benchmark for developing and evaluating computer vision models for pill identification. The ePillID benchmark is designed as a low-shot fine-grained benchmark, reflecting real-world challenges for developing image-based pill identification systems.
The characteristics of the ePillID benchmark include:
- Reference and consumer images: The reference images are taken with controlled lighting and backgrounds, and with professional equipment. The consumer images are taken with real-world settings including different lighting, backgrounds, and equipment. For most of the pills, one image per side (two images per pill type) is available from the NIH Pillbox dataset.
- Low-shot and fine-grained setting: 13k images representing 9804 appearance classes (two sides for 4902 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting.
- The best performing model at the time of publication is a multi-head metric learning approach. Following a CNN encoder, a bilinear transformation layer is used to represent fine-grained embeddings.
Please refer to our paper for more details:
@inproceedings{usuyama2020epillid,
title={ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification},
author={Usuyama, Naoto and Delgado, Natalia Larios and Hall, Amanda K and Lundin, Jessica},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
year={2020}
}
Data
Downloadable from the releases page.
Docker
Docker image https://hub.docker.com/r/naotous/pytorch-image
sudo nvidia-docker pull naotous/pytorch-image:py36torch041-legacy
Train and evaluate models
sudo nvidia-docker run --ipc=host -it -v $HOME:$HOME naotous/pytorch-image:py36torch041-legacy /bin/bash
use -v mount option to access folders from Docker containers
source activate py36
python train_cv.py --appearance_network resnet18 --max_epochs 10 --data_root_dir /home/naotous/ePillID_data
NOTE: max_epochs 10 for quick testing
Train and evaluate models on Azure ML
python azureml_submit_runs.py ePillID_0430
Notes and acknowledgments
The ePillID dataset includes customized data from two public NIH NLM databases. Please refer to their websites for original data:
For bilinear pooling implementations, we forked fast-MPN-Cov GitHub repo.
Please consider citing their work as well when you find our work useful in your research.
Disclaimer
This dataset and software are released for research purposes only.