# Cats and Dogs Classifier

This model is a Convolutional Neural Network (CNN) trained to classify images of cats and dogs. It was trained on the `louiecerv/cats_dogs_dataset` dataset.

## Model Architecture

- 4 Convolutional layers with ReLU activation and Batch Normalization
- MaxPooling layers after each convolutional layer
- Dropout layer after the second convolutional layer
- Flatten layer
- Fully connected layer with 512 units and ReLU activation
- Output layer with softmax activation for 2 classes (cats and dogs)

## Training

The model was trained using the Adam optimizer with a learning rate of 0.001 and Sparse Categorical Crossentropy loss. It was trained for 20 epochs with a batch size of 64. Data augmentation was also used.

## Performance

(Add your model's performance metrics here after training)

## Usage

To use this model, you can load it using TensorFlow/Keras and make predictions on new images of cats and dogs.