Cats vs Dogs Classification using Convolutional Neural Network
Are you a cat person or a dog person? This repository delves into the timeless debate with a robust Convolutional Neural Network (CNN) designed for precise classification of images featuring our beloved furry companions. Whether you're captivated by the graceful poise of cats or charmed by the boundless enthusiasm of dogs, this project showcases advanced deep learning techniques applied to the fascinating realm of pet image classification.
Classifying whether images contain a dog or a cat is straightforward for humans and other animals. However, it requires training over a large dataset for computers to distinguish properly.
The datataset used is the kaggle's Dogs vs Cats dataset, which consists of thousands of images of cats and dogs.
Dataset Source Link: kaggle dataset
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After downloading the dataset, unzip it and place the
testandtrainfolders in the main project directory. -
Clone the repository
git clone https://github.com/priyanshudutta04/Cats-Vs-Dogs.git
- Install dependencies
pip install -r requirements.txt
- Run the Model
jupyter notebook Model_Training.ipynb
Note: If GPU is available install cuda toolkit and cuDNN for faster execution
Contributions are welcome! If you have ideas for improving the model or adding new features, please feel free to fork the repository and submit a pull request.
This repository and its CNN model are developed solely for educational purposes. While efforts have been made to ensure accuracy within this demonstration, it is not intended for critical or commercial applications where reliability and accuracy are paramount. Users should exercise caution and discretion, as its capabilities may not meet real-world demands. The creators disclaim any responsibility for consequences resulting from the use of this software beyond its intended educational scope.
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