Cats vs Dogs Classification with PyTorch

This repository demonstrates how to use a Convolutional Neural Network (CNN) implemented in PyTorch for classifying images between cats and dogs.

Usage

Requirements

pip install -r requirement.txt

Data

Download the cats and dogs image dataset from the Kaggle competition.

kaggle competitions download -c dogs-vs-cats

Or link: https://www.kaggle.com/c/dogs-vs-cats downloaded file and place it in the root folder of the repository.

Unzip and Split data to Train, Validation.

python get-data.py

get-data.py Run the script to extract and split the data into training and validation sets.

Training

Train the model using the ResNet50 architecture on the dataset. (batch-size = 32, image-size = (64,64), learning-rate = 0.001)

python train.py

Classification

To classify cat and dog images in the test folder, run the script using the trained model from train.py.

python classify.py

Notes

If there are any errors, please check and add frequently asked questions to the instructions.

Ensure the necessary libraries are installed; details can be found in the requirements.txt file.

A Kaggle account is required to download the dataset from the competition.