TensorFlow Lite

GitHub repository ↗ synced 2026/8/9

Awesome TensorFlow Lite

TensorFlow Lite is a set of tools that help convert and optimize TensorFlow models to run on mobile and edge devices. It's currently running on more than 4 billion devices! With TensorFlow 2.x, you can train a model with tf.Keras, easily convert a model to .tflite and deploy it; or you can download a pretrained TensorFlow Lite model from the model zoo.

This is an awesome list of TensorFlow Lite models with sample apps, helpful tools and learning resources -

Showcase what the community has built with TensorFlow Lite
Put all the samples side-by-side for easy reference
Share knowledge and learning resources

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

Computer vision
Text
Speech
Recommendation
Game
TensorFlow Lite models
TensorFlow models
Blog posts
Books
Videos
Podcasts
MOOCs

Past announcements:

Here are some past feature annoucements of TensorFlow Lite:

Announcement of the new converter - MLIR-based and 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. Enabled by default in the nightly builds.
Android Support Library - Makes mobile development easier (Android sample code).
Model Maker - Create your custom image & text classification models easily in a few lines of code. See below the Icon Classifier for a tutorial by the community.
On-device training - It is finally here! Currently limited to transfer learning for image classification only but it's a great start. See the official Android sample code and another one from the community (Blog | Android).
Hexagon delegate - How to use the Hexagon Delegate to speed up model inference on mobile and edge devices. Also see blog post Accelerating TensorFlow Lite on Qualcomm Hexagon DSPs.
Model Metadata - Provides a standard for model descriptions which also enables Code Gen and Android Studio ML Model Binding.

Models with samples

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

Computer vision

Classification

Task	Model	App \| Reference	Source
Classification	MobileNetV1 (download)	Android \| iOS \| Raspberry Pi \| Overview	tensorflow.org
Classification	MobileNetV2	Recognize Flowers on Android Codelab \| Android	TensorFlow team
Classification	MobileNetV2	Skin Lesion Detection Android	Community
Classification	MobileNetV2	American Sign Language Detection \| Colab Notebook \| Android	Community
Classification	CNN + Quantisation Aware Training	Stone Paper Scissor Detection Colab Notebook \| Flutter	Community
Classification	EfficientNet-Lite0 (download)	Icon Classifier Colab & Android \| tutorial 1 \| tutorial 2	Community

Detection

Task	Model	App \| Reference	Source
Object detection	Quantized COCO SSD MobileNet v1 (download)	Android \| iOS \| Overview	tensorflow.org
Object detection	YOLO	Flutter \| Paper	Community
Object detection	YOLOv5	Yolov5 Inference	Community
Object detection	MobileNetV2 SSD (download)	Reference	MediaPipe
Object detection	MobileDet (Paper)	Blog post (includes the TFLite conversion process)	MobileDet is from University of Wisconsin-Madison and Google and the blog post is from the Community
License Plate detection	SSD MobileNet (download)	Flutter	Community
Face detection	BlazeFace (download)	Paper	MediaPipe
Face Authentication	FaceNet	Flutter	Community
Hand detection & tracking	Palm detection & hand landmarks (download)	Blog post \| Model card \| Android	MediaPipe & Community

Segmentation

Task	Model	App \| Reference	Source
Segmentation	DeepLab V3 (download)	Android & iOS \| Overview \| Flutter Image \| Realtime \| Paper	tf.org & Community
Segmentation	Different variants of DeepLab V3 models	Models on TF Hub with Colab Notebooks	Community
Segmentation	DeepLab V3 model	Android \| Tutorial	Community
Hair Segmentation	Download	Paper	MediaPipe

Style Transfer

Task	Model	App \| Reference	Source
Style transfer	Arbitrary image stylization	Overview \| Android \| Flutter	tf.org & Community
Style transfer	Better-quality style transfer models in .tflite	Models on TF Hub with Colab Notebooks	Community
Video Style Transfer	Download: Dynamic range models)	Android \| Tutorial	Community
Segmentation & Style transfer	DeepLabV3 & Style Transfer models	Project repo \| Android \| Tutorial	Community

Generative

Task	Model	App \| Reference	Source
GANs	U-GAT-IT (Selfie2Anime)	Project repo \| Android \| Tutorial	Community
GANs	White-box CartoonGAN (download)	Project repo \| Android \| Tutorial	Community
GANs - Image Extrapolation	Boundless on TF Hub	Colab Notebook \| Original Paper	Community

Post estimation

Task	Model	App \| Reference	Source
Pose estimation	Posenet (download)	Android \| iOS \| Overview	tensorflow.org
Pose Classification based Video Game Control	MoveNet Lightning (download)	Project Repository	Community

Other

Task	Model	App \| Reference	Source
Low-light image enhancement	Models on TF Hub	Project repo \| Original Paper \| Flutter
OCR	Models on TF Hub	Project Repository	Community

Text

Task	Model	Sample apps	Source
Question & Answer	DistilBERT	Android	Hugging Face
Text Generation	GPT-2 / DistilGPT2	Android	Hugging Face
Text Classification	Download	Android \|iOS \| Flutter	tf.org & Community
Text Detection	CRAFT Text Detector (Paper)	Download \| Project Repository \| Blog1-Conversion to TFLite \| Blog2-EAST vs CRAFT \| Models on TF Hub \| Android (Coming Soon)	Community
Text Detection	EAST Text Detector (Paper)	Models on TF Hub \| Conversion and Inference Notebook	Community

Speech

Task	Model	App \| Reference	Source
Speech Recognition	DeepSpeech	Reference	Mozilla
Speech Recognition	CONFORMER	Inference Android	Community
Speech Synthesis	Tacotron-2, FastSpeech2, MB-Melgan	Android	TensorSpeech
Speech Synthesis(TTS)	Tacotron2, FastSpeech2, MelGAN, MB-MelGAN, HiFi-GAN, Parallel WaveGAN	Inference Notebook \| Project Repository	Community

Recommendation

Task	Model	App \| Reference	Source
On-device Recommendation	Dual-Encoder	Android \| iOS \| Reference	tf.org & Community

Game

Task	Model	App \| Reference	Source
Game agent	Reinforcement learning	Flutter \| Tutorial	Community

Model zoo

TensorFlow Lite models

These are the TensorFlow Lite models that could be implemented in apps and things:

MobileNet - Pretrained MobileNet v2 and v3 models.
TensorFlow Lite models
- TensorFlow Lite models - With official Android and iOS examples.
- Pretrained models - Quantized and floating point variants.
- TensorFlow Hub - Set "Model format = TFLite" to find TensorFlow Lite models.

TensorFlow models

These are TensorFlow models that could be converted to .tflite and then implemented in apps and things:

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

Ideas and Inspiration

E2E TFLite Tutorials - Checkout this repo for sample app ideas and seeking help for your tutorial projects. Once a project gets completed, the links of the TensorFlow Lite model(s), sample code and tutorial will be added to this awesome list.

ML Kit examples

ML Kit is a mobile SDK that brings Google's ML expertise to mobile developers.

2019-10-01 ML Kit Translate demo - A tutorial with material design Android (Kotlin) sample - recognize, identify Language and translate text from live camera with ML Kit for Firebase.
2019-03-13 Computer Vision with ML Kit - Flutter In Focus.
2019-02-09 Flutter + MLKit: Business Card Mail Extractor - A blog post with a Flutter sample code.
2019-02-08 From TensorFlow to ML Kit: Power your Android application with machine learning - A talk with Android (Kotlin) sample code.
2018-08-07 Building a Custom Machine Learning Model on Android with TensorFlow Lite.
2018-07-20 ML Kit and Face Detection in Flutter.
2018-07-27 ML Kit on Android 4: Landmark Detection.
2018-07-28 ML Kit on Android 3: Barcode Scanning.
2018-05-31 ML Kit on Android 2: Face Detection.
2018-05-22 ML Kit on Android 1: Intro.

Plugins and SDKs

Edge Impulse - Created by @EdgeImpulse to help you to train TensorFlow Lite models for embedded devices in the cloud.
MediaPipe - A cross platform (mobile, desktop and Edge TPUs) AI pipeline by Google AI. (PM Ming Yong) | MediaPipe examples.
Coral Edge TPU - Edge hardware by Google. Coral Edge TPU examples.
TensorFlow Lite Flutter Plugin - Provides a dart API similar to the TensorFlow Lite Java API for accessing TensorFlow Lite interpreter and performing inference in flutter apps. tflite_flutter on pub.dev.

Helpful links

Netron - A tool for visualizing models.
AI benchmark - A website for benchmarking computer vision models on smartphones.
Performance measurement - How to measure model performance on Android and iOS.
Material design guidelines for ML - How to design machine learning powered features. A good example: ML Kit Showcase App.
The People + AI Guide book - Learn how to design human-centered AI products.
Adventures in TensorFlow Lite - A repository showing non-trivial conversion processes and general explorations in TensorFlow Lite.
TFProfiler - An Android-based app to profile TensorFlow Lite models and measure its performance on smartphone.
TensorFlow Lite for Microcontrollers
TensorFlow Lite Examples - Android - A repository refactors and rewrites all the TensorFlow Lite Android examples which are included in the TensorFlow official website.
Tensorflow-lite-kotlin-samples - A collection of Tensorflow Lite Android example Apps in Kotlin, to show different kinds of kotlin implementation of the example apps

Learning resources

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.

Blog posts

2021-11-09 On-device training in TensorFlow Lite
2021-09-27 Optical character recognition with TensorFlow Lite: A new example app
2021-06-16 https://blog.tensorflow.org/2021/06/easier-object-detection-on-mobile-with-tf-lite.html
2020-12-29 YOLOv3 to TensorFlow Lite Conversion - By Nitin Tiwari.
2020-04-20 What is new in TensorFlow Lite - By Khanh LeViet.
2020-04-17 Optimizing style transfer to run on mobile with TFLite - By Khanh LeViet and Luiz Gustavo Martins.
2020-04-14 How TensorFlow Lite helps you from prototype to product - By Khanh LeViet.
2019-11-08 Getting Started with ML on MCUs with TensorFlow - By Brandon Satrom.
2019-08-05 TensorFlow Model Optimization Toolkit — float16 quantization halves model size - By the TensorFlow team.
2018-07-13 Training and serving a real-time mobile object detector in 30 minutes with Cloud TPUs - By Sara Robinson, Aakanksha Chowdhery, and Jonathan Huang.
2018-06-11 - Why the Future of Machine Learning is Tiny - By Pete Warden.
2018-03-30 - Using TensorFlow Lite on Android) - By Laurence Moroney.

Books

2021-12-01 AI and Machine Learning On-Device Development (early access) - By Laurence Moroney (@lmoroney).
2020-10-01 AI and Machine Learning for Coders - By Laurence Moroney (@lmoroney).
2020-04-06 Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS (GitHub) - By Anubhav Singh (@xprilion) and Rimjhim Bhadani (@Rimjhim28).
2020-03-01 Raspberry Pi for Computer Vision (Complete Bundle | TOC) - By the PyImageSearch Team: Adrian Rosebrock (@PyImageSearch), David Hoffman, Asbhishek Thanki, Sayak Paul (@RisingSayak), and David Mcduffee.
2019-12-01 TinyML - By Pete Warden (@petewarden) and Daniel Situnayake (@dansitu).
2019-10-01 Practical Deep Learning for Cloud, Mobile, and Edge - By Anirudh Koul (@AnirudhKoul), Siddha Ganju (@SiddhaGanju), and Meher Kasam (@MeherKasam).

Videos

2021-10-06 Contributing to TensorFlow Lite with Sunit Roy (Hacktoberfest 2021)
2020-07-25 Android ML by Hoi Lam (GDG Kolkata meetup).
2020-04-01 Easy on-device ML from prototype to production (TF Dev Summit 2020).
2020-03-11 TensorFlow Lite: ML for mobile and IoT devices (TF Dev Summit 2020).
2019-10-31 Keynote - TensorFlow Lite: ML for mobile and IoT devices.
2019-10-31 TensorFlow Lite: Solution for running ML on-device.
2019-10-31 TensorFlow model optimization: Quantization and pruning.
2019-10-29 Inside TensorFlow: TensorFlow Lite.
2018-04-18 TensorFlow Lite for Android (Coding TensorFlow).

Podcasts

2020-08-08 Talking Machine Learning with Hoi Lam.

MOOCs

Introduction to TensorFlow Lite - Udacity course by Daniel Situnayake (@dansitu), Paige Bailey (@DynamicWebPaige), and Juan Delgado.
Device-based Models with TensorFlow Lite - Coursera course by Laurence Moroney (@lmoroney).
The Future of ML is Tiny and Bright - A series of edX courses created by Harvard in collaboration with Google. Instructors - Vijay Janapa Reddi, Laurence Moroney, and Pete Warden.