Computer Vision for Medication Blister Classification Computer Vision Project
Updated a year ago
Metrics
Project Overview: The goal of this project is to develop a computer vision system that can accurately classify medication blisters as either full or empty. This system will aid in automating the verification process of medication blister packs, ensuring the correct dispensing of medications in healthcare settings. By leveraging machine learning techniques and image analysis algorithms, the system will be trained on a dataset of labeled images to achieve high accuracy in blister classification.
Class Types:
Full Blister: This class represents medication blisters that contain the intended number of pills or capsules. Images labeled as "full" will be used as positive examples during the training phase.
Empty Blister: This class represents medication blisters that do not contain any pills or capsules. Images labeled as "empty" will serve as negative examples during training.
Use This Trained Model
Try it in your browser, or deploy via our Hosted Inference API and other deployment methods.
Build Computer Vision Applications Faster with Supervision
Visualize and process your model results with our reusable computer vision tools.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
computer-vision-for-medication-blister-classification_dataset,
title = { Computer Vision for Medication Blister Classification Dataset },
type = { Open Source Dataset },
author = { KNOK },
howpublished = { \url{ https://universe.roboflow.com/knok/computer-vision-for-medication-blister-classification } },
url = { https://universe.roboflow.com/knok/computer-vision-for-medication-blister-classification },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { aug },
note = { visited on 2024-09-27 },
}