Pet Classifier is a deep learning model that classifies images of cats and dogs. It is trained using a Convolutional Neural Network (CNN) architecture and achieves high accuracy on the test data.
The dataset consists of a collection of images of pets, including cats and dogs. The images have different sizes and lighting conditions. The dataset is split into training and test sets.
The images are preprocessed using data augmentation techniques such as random shearing, zooming, and horizontal flipping. The pixel values are normalized to the range [0, 1]. This helps in improving the model's performance and generalization.
The model is based on a CNN architecture. It consists of two convolutional layers with pooling, followed by a dense layer and a dropout layer. The final layer uses the softmax activation function for multi-class classification.
The model is trained on the training data with the Adam optimizer and categorical cross-entropy loss. The accuracy and loss are monitored during training. The model is evaluated on the test data to assess its performance.
