CatDog-Image-Classification is a machine learning project that aims to classify images into two categories: cats and dogs. The project utilizes deep learning techniques to create a model that can distinguish between images of cats and dogs accurately.

• Image Classification: The model can classify images into two classes: cats and dogs.
• Accuracy: The model is trained on a dataset of cat and dog images to ensure high accuracy in classification.
• Jupyter Notebook: You can explore the model training process and evaluation in the provided Jupyter Notebook.
• Test Images: Sample cat and dog images are included in the repository for testing the trained model.

To get started with the CatDog-Image-Classification project, follow these steps:

1. Clone the repository:

   git clone https://github.com/Dishantkharkar/CatDog-Image-Classification.git

2. Open and explore the Jupyter Notebook CatDog_Image_Classification.ipynb to understand the model training process.

3. Use the model to classify images of cats and dogs using the provided test images (cat.jpg and dog.jpg).

The dataset used for training and testing the model is available in the https://www.kaggle.com/datasets/salader/dogs-vs-cats. It contains a collection of labeled cat and dog images.

Contributions to the CatDog-Image-Classification project are welcome. If you would like to contribute, please follow our Contribution Guidelines.

This project is licensed under the MIT License - see the LICENSE file for details.

For any questions or inquiries, please contact the project maintainer:

• Name: Dishant Kharkar
• Email: [email protected]

We would like to thank the open-source community and all contributors for their support and contributions to this project.

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