Deploy machine learning models on mobile and IoT devices
TensorFlow Lite is an open source deep learning framework for on-device inference.
How it works
Pick a model
Pick a new model or retrain an existing one.
Convert a TensorFlow model into a compressed flat buffer with the TensorFlow Lite Converter.
Take the compressed .tflite file and load it into a mobile or embedded device.
Quantize by converting 32-bit floats to more efficient 8-bit integers or run on GPU.
Solutions to common problems
Explore optimized models to help with common mobile and edge use cases.
Identify hundreds of objects, including people, activities, animals, plants, and places.
Detect multiple objects with bounding boxes. Yes, dogs and cats too.
Use a state-of-the-art natural language model to answer questions based on the content of a given passage of text with BERT.
Arm’s engineers have developed optimized versions of the TensorFlow Lite kernels that use CMSIS-NN to deliver blazing fast performance on Arm Cortex-M cores.
The task of recovering a high resolution (HR) image from its low resolution counterpart is commonly referred to as Single Image Super Resolution (SISR). In this tutorial, we use a pre-trained ESRGAN model from TensorFlow Hub and generate super resolution images using...
We are excited to announce that Teachable Machine now allows you to train your own sound classification model and export it in the TensorFlow Lite (TFLite) format. Then you can integrate the TFLite model to your mobile applications or your IoT devices. This is an easy...
Learn how to train and deploy an ML model on an Android app in just a few lines of code with TensorFlow Lite Model Maker and Android Studio. From here you can then explore how to use various tools from Google to turn a prototype into a production app. Presented by...