FelineFinder - Cat Recognition System

FelineFinder is a Python-based image recognition system designed to identify whether an image contains a cat with up to 80% accuracy. What sets FelineFinder apart is its flexibility, as it can be easily customized to recognize other objects or animals by modifying the dataset.

Key Features

• High Accuracy: FelineFinder achieves an accuracy rate of up to 80% in distinguishing cats from other objects or scenes, making it a valuable tool for cat identification.

• Customizable Datasets: Easily adapt the system to recognize other objects or animals by customizing the dataset, making it versatile for various recognition tasks.

• Deep Learning Technology: FelineFinder utilizes cutting-edge deep learning algorithms to extract features and patterns from images, enabling continuous improvements in recognition accuracy.

• Scalability: FelineFinder's architecture is designed to handle larger and more diverse datasets, ensuring it can adapt to evolving recognition needs.

• Real-World Applications: Beyond cat lovers, FelineFinder finds use in wildlife monitoring, pet shelter management, and pet-centric mobile apps.

• Comprehensive Documentation: Access detailed installation guides, tutorials, and best practices for dataset customization to maximize the system's capabilities.

Getting Started

1. Clone this repository to your local machine.

2. Add your desired dataset of cat and non-cat images to the datasets folder.

3. Place the image you want to compare in the images folder.

4. Open the Python script, replace the placeholder in the my_image section with the filename of the image you want to analyze.

5. Run the script to get the results. Remember that FelineFinder has an accuracy of up to 80%.

Future Enhancements

• Multi-Object Recognition: Expand the system's capabilities to recognize multiple objects or animals within the same image.

• Real-Time Processing: Implement real-time image recognition for applications like pet surveillance systems or mobile apps.

• Cloud Integration: Enable cloud-based recognition to handle large-scale image analysis projects.

• User Feedback Integration: Allow users to provide feedback on recognition results for continuous model refinement.

License

This project is licensed , usage of this for your interview purpose may lead to consequences, feel free to use it's code as a part of your project but by giving credits to Puneet Bajaj.

Contributing

We welcome contributions from the community.

Acknowledgments

We would like to thank the Deep Learning community for their valuable contributions and support in the development of FelineFinder.