The following samples demonstrate the use of TensorFlow Lite in mobile applications. Each sample is written for both Android and iOS.
This app performs image classification on a live camera feed and displays the inference output in realtime on the screen.
This app performs object detection on a live camera feed and displays the results in realtime on the screen. The app displays the confidence scores, classes and detected bounding boxes for multiple objects. A detected object is only displayed if the confidence score is greater than a defined threshold.
This application recognizes a set of voice commands using the device's microphone input. When a command is spoken, the corresponding class in the app is highlighted.
This app uses a model to classify and recognize different gestures. A model is trained on webcam data captured using a web interface. The model is then converted to a TensorFlow Lite model and used to classify gestures in a mobile application.
First, we use TensorFlow.js embedded in a web interface to collect the data required to train the model. We then use TensorFlow.js to train the model.
The model downloaded from the web interface is converted to a TensorFlow Lite model.
Conversion script (available as a Colab notebook).
Once we have the TensorFlow Lite model, the implementation is very similar to the Image classification sample.