Dataset Card for Pills Inside Bottles

The dataset contains 13,955 imgaes of pills inside medication bottles, their National Drug Code (NDC), and image id.

Dataset Details

Dataset Description

The dataset contains 13,955 imgaes of pills inside medication bottles, which are from a top down view. They are labeled with 20 distinct National Drug Code (NDC) and each image is associated with an image id. The dataset is split into train, test, and validation sets.

License: cc-by-4.0

Dataset Sources

Homepage: Images of pills inside medication bottles dataset
Paper: Performance evaluation of a prescription medication image classification model: an observational cohort

Uses

Direct Use

The dataset can be used to train image classification models, which might be helpful for identifying pills automatically to increase efficiency and reduce dispensing error of pills in pharmacy.

Example Use Case

Here is a link to an example use case of this dataset, which trained supervised models to predict the national drug code for each image. The highest accuracy obtained is about 97% using EfficientNetV2M as the pre-train model and Support Vector Machines as the supervised learning algorithm.

Dataset Structure

Data Instances

Each data instance contains the following features: image, ndc, and id.

An example from the train set looks like the following:

{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1284x960>,
 'ndc': '29159',
 'id': '00378-3855'}

Data Fields

'image': a image of pills inside a medication bottle
'ndc': National Drug Code
'id': unique image id

Data Splits

The dataset has 3 splits: train, validation, and test. The splits contain disjoint sets of images as well as their corresponding NDCs and IDs. The following table contains the number of images in each split and the percentages.

Dataset Split	Number of Instances in Split	Percent
Train	8,393	60.1%
Validation	2,786	20.0%
Test	2,776	19.9%

Dataset Creation

Curation Rationale

The data is collected for training image classification model to facillitate work in pharmacy.

Source Data

Data Collection and Processing

The data is collected by a group of researchers, including Lester, C. A., Al Kontar, R., and Chen, Q., whose paper, "Performance Evaluation of a Prescription Medication Image Classification Model: An Observational Cohort", is published in 2022.

Who are the source data producers?

According to Lester et. al, the dataset is produced by a commercial medication dispensing robot used at a mail-order pharmacy from a top-down view (Lester et. al, 6).

Bias, Risks, and Limitations

The researchers of the original paper only released part of their data. Therefore, the model performance might be influenced negatively due to insufficient training data.

Citation

BibTeX:

@InProceedings{University of Michigan - Deep Blue Data,
title = {Images of pills inside medication bottles dataset},
author={Lester, C. A., Al Kontar, R., Chen, Q.},
year={2022}
}

More Information

This dataset contain all information from the source data. The only change made is to rearrange the structure by extracting the file names, which correspond to NDC and image id, and put them into separated columns.