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imageclassification

Image Classification Android App

Description

The Image Classification Android App is an application that utilizes machine learning algorithms to identify images. With the help of pre-trained models, this app can provide accurate predictions for various image formats, such as JPEG and PNG. The user-friendly interface allows for easy image selection from either the device's camera or gallery. The app displays the top predicted labels for each image, giving users insights into the classification results.

Features

  • Image classification using state-of-the-art machine learning models.
  • Support for popular image formats like JPEG and PNG.
  • Real-time classification or batch classification options.
  • Intuitive user interface with easy image selection.
  • Capture images directly from the device's camera or choose from the gallery.
  • Displaying the top predicted labels for each image.
  • Accuracy metrics or confidence scores for classification results.

Requirements

  • Minimum Android version: Android 5.0 (Lollipop) or above.
  • Required permissions: Camera access, gallery access.

Usage

  1. Launch the Image Classification Android App on your device.
  2. On the main screen, you will see options to either capture an image using the camera or select an image from the gallery.
  3. Choose your preferred method to select an image.
  4. Once an image is selected, the app will start the classification process.
  5. After a few seconds, the app will display the top predicted labels for the image along with their corresponding confidence scores.
  6. To classify another image, use the back button or follow the same process as described above.

Known Issues

  • The app may experience occasional slowdowns when classifying images with large file sizes.
  • Certain image formats or file types may not be supported by the app.

Credits and Acknowledgments

  • This app utilizes the following open-source libraries: TensorFlow_Lite,
  • Keras.
  • The pre-trained models used in this app are provided by VGG-16.

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