A free source code download link is provided at the end of the article
In the critical area of medical diagnosis, fast and accurate image classification plays a vital role in aiding the decision-making of healthcare professionals. The advent of deep learning, coupled with powerful frameworks such as PyTorch, has made it possible to apply cutting-edge models to handle complex tasks such as medical multi-label image classification. In this demonstration, we will use a subset of Kaggle's Human Protein Atlas Image Classification dataset to demonstrate these concepts and address the associated challenges.
We will explore the use of PyTorch with the widely used pytorch-lightning library to fine-tune torchvision's pretrained EfficientNetv2 model. Additionally, we will demonstrate how to create a Gradio application for model inference.
