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Image Classifier Web App

This project is a web application that allows you to upload images and classify them using a image classification model based on TensorFlow.js and MobileNet. It also offers the option to access the device's camera and perform real-time classifications.

Demo

You can view a live demo of the application here.

Features

  • Upload an image from your device.
  • Classify the uploaded image.
  • View classification predictions.
  • Access the camera and perform real-time classifications. (in development)
  • Start and stop the camera.
  • Reset the image.
  • Usage instructions.

Site screenshots

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Requirements

  • Web browser with support for navigator.mediaDevices.getUserMedia to access the camera (Google Chrome is recommended for camera functionality).
  • Internet connection (to load classification models).

Setup

  1. Clone this repository to your local machine:

    git clone YOUR_REPO_URL

  2. Open the index.html file in your web browser.

Usage

  • Upload an image from your device by clicking the "Upload Image" button.
  • Click "Classify Image" to get predictions.
  • You can view the predictions at the bottom of the page.
  • If you need to reload the image, click "Reset."
  • To access the camera, click "Start Camera." You can stop the camera by clicking the same button again.

If you have any questions or need assistance, you can check the "Usage Instructions" by clicking the corresponding button.

Contributions

If you want to contribute to this project, please follow these steps:

  1. Fork the project.

  2. Create a branch with your changes:

`bash git checkout -b feature/your-feature

  1. Make your changes and commit them:

`bash git commit -m 'Add your feature'

  1. Push your changes to your fork:

`bash git push origin feature/your-feature

  1. Open a pull request in this repository.

License

This project is under the MIT License. See the LICENSE file for more details.

Contact

If you have questions or comments about this project, feel free to get in touch with us at [carlos.zendejasmtz@gmail.com].