This Android app utilizes TensorFlow Lite, a framework for deploying machine learning models on mobile devices, to perform image classification. Image classification is the task of identifying what an image represents, and TensorFlow Lite provides optimized pre-trained models for deployment in mobile applications. The app allows users to capture or select an image from their device, and it provides information or performs actions based on the recognized content.
During training, an image classification model is fed images and their associated labels. Each label represents a distinct concept or class that the model learns to recognize. TensorFlow Lite offers optimized pre-trained models, allowing developers to deploy powerful image classification models in mobile apps.
Given sufficient training data, an image classification model can predict whether new images belong to any of the classes it has been trained on. This process, called inference, enables the app to recognize and classify images. Additionally, TensorFlow Lite supports transfer learning, which allows the identification of new image classes without requiring an extensive training dataset.
- Image selection from the device
- Image classification using TensorFlow Lite
- Display of classification results
- Android Studio
- Kotlin
- Basics ML concepts
- Clone the repository: https://github.com/khushi57/Image-Recognition-Android-app.git
- Open the project in Android Studio.
- Build and run the app on an Android device or emulator.
- Launch the app on your Android device.
- Select an image.
- View the classification results.
Contributions are welcome!