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Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, which is ideal for learning and practical use.\n![image](https://github.com/user-attachments/assets/b7745551-7183-4599-9b8e-acf795afc557)\n\n## Key Features\n\n- **Dual Model Support**:\n  - **MobileNetV2 (ImageNet)**: Recognizes 1,000 different classes from the ImageNet dataset, including everyday objects, animals, and vehicles.\n  - **Custom CIFAR-10 Model**: Specializes in classifying images into one of ten specific categories such as airplanes, automobiles, and birds.\n\n- **Intuitive Interface**:\n  - **Navigation Bar**: Seamlessly switch between MobileNetV2 and CIFAR-10 models using a sleek sidebar menu.\n  - **Real-Time Classification**: Upload an image to receive immediate predictions with confidence scores.\n\n- **Educational and Practical Use**:\n  - Ideal for learning about deep learning models and their performance.\n  - Useful for practical applications where image classification is needed.\n\n## Getting Started\n\n![image](https://github.com/user-attachments/assets/e0c3afda-9522-4ad1-91fd-bba331434370)\n\n\n### Usage\n  1. Use the navigation bar to select either the MobileNetV2 or CIFAR-10 model.\n  2. Upload an image file (JPG or PNG).\n  3. View the classification results and confidence scores.\n\n### Contributing\n  Feel free to fork the repository, open issues, or submit pull requests to contribute to the project.\n\n### Acknowledgements\n  - Streamlit\n  - TensorFlow\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmainakverse%2Fvirtual-eye","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmainakverse%2Fvirtual-eye","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmainakverse%2Fvirtual-eye/lists"}