How I Build Cat vs Dog Classifier

In this project, I built a Cat vs Dog image classifier using deep learning and Python. The goal was to train a model that can accurately identify whether an image contains a cat or a dog.

I used TensorFlow and Keras to build a Convolutional Neural Network (CNN). The dataset was preprocessed by resizing images to a fixed size and normalizing pixel values. After training the model, I achieved good accuracy on validation data.

To make the project more interactive, I deployed it using Streamlit. Users can upload an image or capture one using a camera, and the model instantly predicts the result.

This project helped me understand image classification, CNN architecture, and model deployment in a real-world application.