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TensorFlow Lite (TFLite) is a set of tools that help convert and optimize TensorFlow models to run on mobile and edge devices - currently running on more than 3 billion devices! With TensorFlow 2.0, you can train a model with tf.Keras, easily convert it to TFLite and deploy it; or you can download a pretrained TFLite model from the model zoo.

This is a curated list of TFLite models with sample apps, model zoo, helpful tools and learning resources. The purpose of this repo is to -

• showcase what the community has built with TFLite
• put all the samples side-by-side for easy references
• knowledge sharing and learning for everyone

Please submit a PR if you would like to contribute and follow the guidelines here.

Here are some new features recently announced at TensorFlow World:

• New MLIR-based TFLite converter - enables conversion of new classes of models such as Mask R-CNN and Mobile BERT etc, supports functional control flow and better error handling during conversion. Read details in this email announcement.
• TFLite Android Support Library - documentation | Sample code (Android)
• Transfer learning made easy with the Model Customization Toolkit - Colab tutorials: Image | Text
• On-device training is finally here! Currently limited to transfer learning for image classification only but it's a great start - Blog | Sample code (Android)
• Accelerating TensorFlow Lite on Qualcomm Hexagon DSPs - Blog | Documentation

Here are the TFLite models with app / device implementations, and references.
Note: pretrained TFLite models from MediaPipe are included, which you can implement with or without MediaPipe.

TFLite models that could be implemented in apps and things:

• MobileNet- pretrained MobileNet v2 and v3 models.
• TFLite models
  • TensorFlow Lite models with Android and iOS examples
  • TensorFlow Lite hosted models with quantized and floating point variants
  • TFLite models from TensorFlow Hub

TensorFlow models that could be converted to TFLite and then implemented in apps and things:

• Official TensorFlow models
• Tensorflow detection model zoo - pre-trained on COCO, KITTI, AVA v2.1, iNaturalist Species datasets

• 2/9/219 Flutter + MLKit: Business Card Mail Extractor - tutorial | Flutter
• 2/8/2019 From TensorFlow to ML Kit: Power your Android application with machine learning - slides | Android (Kotlin)
• 8/7/2018 Building a Custom Machine Learning Model on Android with TensorFlow Lite - tutorial
• 7/27/2018 ML Kit on Android 4: Landmark Detection - tutorial
• 7/28/2018 ML Kit on Android 3: Barcode Scanning - tutorial
• 5/31/2018 ML Kit on Android 2: Face Detection - tutorial
• 5/22/2018 ML Kit on Android 1: Intro - tutorial

• Coral Edge TPU examples
• MediaPipe examples: mobile, desktop and Edge TPUs
• TensorFlow Lite for Microcontrollers

• Netron - for visualizing models
• AI benchmark - for benchmarking computer vision models on smartphones
• Performance benchmarks for Android and iOS
• How to design machine learning powered features - material design guidelines for ML | ML Kit Showcase App
• The People + AI Guide book - learn how to design human-centered AI products

Interested but not sure how to get started? Here are some learning resources that will help you whether you are a beginner or a practitioner in the field for a while.

• Official TensorFlow Lite documentation (link)

• 11/8/2019 - Getting Started with ML on MCUs with TensorFlow (link)
• 8/5/2019 - TensorFlow Model Optimization Toolkit — float16 quantization halves model size (link)
• 7/13/2018 - Training and serving a realtime mobile object detector in 30 minutes with Cloud TPUs (link)
• 6/11/2018 - Why the Future of Machine Learning is Tiny (link)
• 3/30/2018 - Using TensorFlow Lite on Android (link)

• 12/2019 - TinyML by Pete Warden (@petewarden), Daniel Situnayake (@dansitu)
• 10/2019 - Practical Deep Learning for Cloud, Mobile, and Edge by Anirudh Koul (@AnirudhKoul), Siddha Ganju (@SiddhaGanju), and Meher Kasam (@MeherKasam)

• 10/31/2019 - Keynote - TensorFlow Lite: ML for mobile and IoT devices
• 10/31/2019 - TensorFlow Lite: Solution for running ML on-device
• 10/31/2019 - TensorFlow model optimization: Quantization and pruning
• 10/29/2019 - Inside TensorFlow: TensorFlow Lite
• 4/18/2018 - TensorFlow Lite for Android (Coding TensorFlow)

• Udacity Introduction to TensorFlow Lite - by Daniel Situnayake (@dansitu), Paige Bailey (@DynamicWebPaige), and Juan Delgado
• Coursera Device-based Models with TensorFlow Lite - by Laurence Moroney (@lmoroney)