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.
- What is new
- Models with samples
- Model zoo
- Ideas and Inspiration
- ML Kit examples
- Plugins and SDKs
- Helpful links
- Learning resources
Here are the new features and tools 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.
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.
| 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 | EfficientNet-Lite0 (download) | Icon Classifier Colab & Android | tutorial 1 | tutorial 2 | Community |
| Object detection | Quantized COCO SSD MobileNet v1 (download) | Android | iOS | Overview | tensorflow.org |
| Object detection | YOLO | Flutter | Paper | Community |
| Object detection | MobileNetV2 SSD (download) | Reference | MediaPipe |
| License Plate detection | SSD MobileNet (download) | Flutter | Community |
| Face detection | BlazeFace (download) | Paper | MediaPipe |
| Hand detection & tracking | Palm detection & hand landmarks (download) | Blog post | Model card | MediaPipe |
| Pose estimation | Posenet (download) | Android | iOS | Overview | tensorflow.org |
| 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 |
| Hair Segmentation | Download | Paper | MediaPipe |
| 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 |
| GANs | U-GAT-IT (Selfie2Anime) | Project repo | Android | Tutorial | Community |
| GANs | White-box CartoonGAN (download) | Project repo | Android | Tutorial | Community |
| Video Style Transfer | Download: Dynamic range models) |
Android | Tutorial | Community |
| 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 |
| Task | Model | App | Reference | Source |
|---|---|---|---|
| Speech Recognition | DeepSpeech | Reference | Mozilla |
| Speech Synthesis | Tacotron-2, FastSpeech2, MB-Melgan | Android | TensorSpeech |
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.
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.
- 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 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.
- Edge Impulse - Created by @EdgeImpulse to help you to train TensorFlow Lite models for embedded devices in the cloud.
- Fritz.ai - An ML platform by @fritzlabs that makes mobile development easier: with pre-trained ML models and end-to-end platform for building and deploying custom trained models.
- 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.
- 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
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.
- 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.
- 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).
- 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).
- 2020-08-08 Talking Machine Learning with Hoi Lam.
- 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).