This project implements an image classification model using a Tiny VGG16-inspired Convolutional Neural Network (CNN) trained on a subset of the Food-101 dataset, focusing on just three categories: Pizza, Steak, and Sushi.
🧠 Model Architecture • A compact, VGG16-inspired CNN (Tiny VGG) • Stack of convolutional layers + ReLU + max pooling • Fully connected (dense) layers for classification
📦 Dataset • Subset of Food-101 dataset with 3 classes: Pizza, Steak, and Sushi • Around 255 training images and 75 testing images per class (as per original split)
⚙️ Libraries Used • Python • PyTorch & Torchvision • Matplotlib, NumPy