Cat vs Dog Classification Model

This is a deep learning project developed from scratch to classify images of cats and dogs using a Convolutional Neural Network (CNN). The goal is to train a model that can take an image as input and accurately predict whether it's a cat or a dog.

Features

• Built using TensorFlow and Keras
• Trained on a dataset of 10,000+ images
• Includes scripts for training and prediction
• Supports image input via OpenCV
• Model saved in HDF5 (.h5) format for reuse
• Visualization of test images and predictions using Matplotlib

Project Structure

• Dataset/
  • train/
  • test/
• dog_cat_model.h5
• cat-dog-classification.py
• train_model.py

Requirements

This project was built and tested on an Ubuntu environment.

Key Python Libraries

• tensorflow==2.19.0
• keras==3.9.2
• opencv-python==4.11.0.86
• matplotlib==3.10.1
• numpy==2.1.3
• h5py
• protobuf
• absl-py
• and more...

All required packages are listed in the requirements list in your environment.

How to Use

1. Train the model using the train_model.py script.
2. Save the trained model as dog_cat_model.h5.
3. Use predict.py to load the saved model and predict new images.
4. Ensure that test images are correctly placed and accessible.

Output

The model returns either:

• "Cat" if the image is classified as a cat
• "Dog" if the image is classified as a dog

A visual output of the prediction image will also be displayed using Matplotlib.

Notes

• Make sure you deactivate your virtual environment using deactivate before pushing to GitHub (do not push the venv folder).
• Ensure image paths are valid and images are readable before running prediction.

Made with ❤️ By Ronnie Allen.