Cat and Dog Recognition AI Model

A simple Convolutional Neural Network (CNN) built with TensorFlow/Keras to distinguish between cats and dogs.

Project Structure

• download_images.py: Downloads 100 images (50 cats, 50 dogs) from public APIs.
• train_model.py: Preprocesses images and trains the CNN model.
• predict.py: Script to run inference on individual images.
• requirements.txt: List of dependencies.
• cat_dog_model.h5: The trained model file (generated after training).

Setup Instructions

1. Install Dependencies

pip install -r requirements.txt

2. Train the Model

Train the AI model on the downloaded images:

python train_model.py

3. Run Predictions

Use the prediction script to identify an image:

python predict.py path/to/your/image.jpg

How it Works

The model uses a sequential CNN architecture with:

• Rescaling layer (normalization)
• 3 Convolutional blocks with MaxPooling
• A Dense layer with 128 units
• A final output layer for 2 classes (Cats, Dogs)