🐱 Cat vs. Dog Classifier 🐶

This project is a Convolutional Neural Network (CNN) model that can distinguish between cats and dogs in images. Inspired by tutorials and articles, such as Cat & Dog Classification Neural Network in Python, this project was designed as an introduction to using AI models with Python.

Ultimately, the goal was to familiarize myself with building, training, and deploying AI models—starting with a simple binary classification task: Cats vs. Dogs.

Is it cat? Is it dog? Or is it something in between?

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🚀 Features

• CNN: A straightforward Convolutional Neural Network which knows the difference between a dog and a cat.
• Image Preprocessing: Resizing, normalization, and batching for preparing image data.
• Model Evaluation: Track performance during training, including accuracy and loss metrics.
• Saving & Loading: The trained model can be saved and reloaded for future predictions.

Cat vs Dog Model Results

If you for some reason have never seen a cat or dog before or struggle to tell the difference this is a helpful tool to help you figure out what it is.

🚨 CAT SPOTTED! 🚨

🚨 DOG SPOTTED! 🚨

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💻 Getting Started

To get started with this project, follow these steps:

1. Clone the Repository

git clone https://github.com/yourusername/cat-vs-dog-classifier.git
cd cat-vs-dog-classifier

2. Create a Virtual Environment

python -m venv venv
source venv/bin/activate # On Window, use `venv/Scripts/activate`

3. Install Dependencies

pip install -r requirements.txt

📸 Using the Model

After setting up the environment and installing dependencies, you can begin using the model. Follow these steps:

1. Train The Model

Run train_model this will train the model using Tensorflow dataset

python train_model.py

2. Prepare Your Images

Place your cat and dog images the images folder Change the image_path = 'images/image1.jpg' to your desired image

3. Run the Model

Run the run_model.py script and view the results in the terminal

python run_model.py