This is an example application for TensorFlow Lite on Android. It uses Image classification to continuously classify whatever it sees from the device's back camera. Inference is performed using the TensorFlow Lite Java API. The demo app classifies frames in real-time, displaying the top most probable classifications. It allows the user to choose between a floating point or quantized model, select the thread count, and decide whether to run on CPU, GPU, or via NNAPI.

These instructions walk you through building and running the demo on an Android device. For an explanation of the source, see TensorFlow Lite Android image classification example.

Inside Assests folder zip file is there.

Resnet50 16 batch size 100 epochs Teachable ML

• Android Studio 3.2 (installed on a Linux, Mac or Windows machine)

• Android device in developer mode with USB debugging enabled

• USB cable (to connect Android device to your computer)

Do not delete the assets folder content. If you explicitly deleted the files, choose Build -> Rebuild to re-download the deleted model files into the assets folder.