https://github.com/mainakverse/virtual-eye

Virtual Eye is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. 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.
https://github.com/mainakverse/virtual-eye

cifar-10 image-processing mobilenetv2 neon object-detection streamlit

Last synced: 7 months ago
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Virtual Eye is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. 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.

• Host: GitHub
• URL: https://github.com/mainakverse/virtual-eye
• Owner: MainakVerse
• Created: 2025-03-01T11:30:34.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-03-01T11:44:39.000Z (7 months ago)
• Last Synced: 2025-03-01T12:25:53.560Z (7 months ago)
• Topics: cifar-10, image-processing, mobilenetv2, neon, object-detection, streamlit
• Language: Python
• Homepage: https://virtual-eye.streamlit.app/
• Size: 0 Bytes
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Virtual Eye

Virtual Eye is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. 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.

![image](https://github.com/user-attachments/assets/b7745551-7183-4599-9b8e-acf795afc557)

## Key Features

- **Dual Model Support**:

  - **MobileNetV2 (ImageNet)**: Recognizes 1,000 different classes from the ImageNet dataset, including everyday objects, animals, and vehicles.

  - **Custom CIFAR-10 Model**: Specializes in classifying images into one of ten specific categories such as airplanes, automobiles, and birds.

- **Intuitive Interface**:

  - **Navigation Bar**: Seamlessly switch between MobileNetV2 and CIFAR-10 models using a sleek sidebar menu.

  - **Real-Time Classification**: Upload an image to receive immediate predictions with confidence scores.

- **Educational and Practical Use**:

  - Ideal for learning about deep learning models and their performance.

  - Useful for practical applications where image classification is needed.

## Getting Started

![image](https://github.com/user-attachments/assets/e0c3afda-9522-4ad1-91fd-bba331434370)

### Usage

  1. Use the navigation bar to select either the MobileNetV2 or CIFAR-10 model.

  2. Upload an image file (JPG or PNG).

  3. View the classification results and confidence scores.

### Contributing

  Feel free to fork the repository, open issues, or submit pull requests to contribute to the project.

### Acknowledgements

  - Streamlit

  - TensorFlow