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# CVNets: A library for training computer vision networks
Enhanced MobileViT for Medical Image Classification (Adapted Using CVNets Library)

CVNets is a computer vision toolkit that allows researchers and engineers to train standard and novel mobile-
and non-mobile computer vision models for variety of tasks, including object classification, object detection,
semantic segmentation, and foundation models (e.g., CLIP).
This project adapts the CVNets computer vision toolkit to train an enhanced version of the MobileViT architecture specifically for medical image classification. While CVNets is a powerful library designed for training both mobile and non-mobile vision models across tasks such as image classification, detection, segmentation, and foundation models (e.g., CLIP), our work customizes it for disease detection using the NIH Malaria Cell Images Dataset.

By integrating the MobileViT framework with domain-specific enhancements—including medical-appropriate data augmentation, multi-scale feature fusion, and attention mechanisms (CBAM)—this project demonstrates how CVNets can be extended beyond general-purpose datasets (e.g., ImageNet) to support high-impact medical imaging applications. The updated system enables efficient and accurate classification suitable for use in clinical settings and on resource-constrained devices.
## Table of contents

* [What's new?](#whats-new)
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