This repository demonstrates how to use a Convolutional Neural Network (CNN) implemented in PyTorch for classifying images between cats and dogs.
pip install -r requirement.txt
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.
python get-data.py
get-data.py Run the script to extract and split the data into training and validation sets.
Train the model using the ResNet50 architecture on the dataset. (batch-size = 32, image-size = (64,64), learning-rate = 0.001)
python train.py
To classify cat and dog images in the test folder, run the script using the trained model from train.py.
python classify.py
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.