This project uses a TinyVGG16-based CNN to classify MRI scans for Alzheimer's Disease stages: Mild Impairment, Moderate Impairment, No Impairment, and Very Mild Impairment. It includes Jupyter notebooks for training and prediction, and a Streamlit app for easy inference. The model achieves high metrics in predicting Alzheimer's stages.

The Ocular Disease Detection project is an AI-powered web application designed to detect common ocular diseases from digital images. Built with PyTorch and Streamlit, the application uses a custom-trained Convolutional Neural Network (CNN) to classify images into six distinct categories: AMD, Cataract, Glaucoma, Myopia, Normal and non eye images

Lightweight TinyVGG Inspired CNN 1.35M Parameter with Squeeze-and-Excitation blocks for ocular disease classification

TinyVGG model with PyTorch

Food image classification using PyTorch with TinyVGG and EfficientNetB0 on a subset of the Food101 dataset.

This notebook demonstrates the process of building and training a convolutional neural network (CNN) to classify images from the Fashion MNIST dataset. The Fashion MNIST dataset consists of 60,000 training images and 10,000 testing images of clothing items, each labeled with one of 10 categories.

This is the repository for kaggle competition conducted by Annam AI. Contains solutions for 2 challenges. Rank - 4

Hi, this a little notebook of a Computer Vision Neural Network to make predictions on three classes (kind of rubbish) using a model based on the TinyVGG.