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https://github.com/priyanshudutta04/cats-vs-dogs
Cats vs Dogs Classification using CNN
https://github.com/priyanshudutta04/cats-vs-dogs
cats-vs-dogs convolutional-neural-networks deep-learning keras tensorflow
Last synced: about 2 months ago
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Cats vs Dogs Classification using CNN
- Host: GitHub
- URL: https://github.com/priyanshudutta04/cats-vs-dogs
- Owner: priyanshudutta04
- License: mit
- Created: 2024-07-11T15:39:57.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-07-17T15:07:13.000Z (6 months ago)
- Last Synced: 2024-07-17T18:42:56.183Z (6 months ago)
- Topics: cats-vs-dogs, convolutional-neural-networks, deep-learning, keras, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 2.61 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cats Vs Dogs
Cats vs Dogs Classification using Convolutional Neural Network
## About
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.
## Data
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](https://www.kaggle.com/datasets/salader/dogs-vs-cats)
## Usage
1. After downloading the dataset, unzip it and place the `test` and `train` folders in the main project directory.
2. Clone the repository
```
git clone https://github.com/priyanshudutta04/Cats-Vs-Dogs.git
```
3. Install dependencies
```
pip install -r requirements.txt
```
4. Run the Model
```
jupyter notebook Model_Training.ipynb
```
*Note: If GPU is available install `cuda toolkit` and `cuDNN` for faster execution*
## Contributing
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.
## Disclaimer
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.
## Support
If you like this project, do give it a ⭐and share it with your friends