This project uses TensorFlow and deep learning to classify images of food into one of 101 categories from the popular Food-101 dataset. The goal is to train a model that can recognize food items from an image input — making it useful for applications like smart restaurant menus, food logging apps, or nutrition tracking.
- 🧠 Built a Convolutional Neural Network (CNN) using TensorFlow and Keras
- 🗂️ Trained on the Food-101 dataset with 101 food categories
- 🧪 Evaluated accuracy and fine-tuned the model for better performance
- 🖼️ Accepts food images as input and returns the predicted food label