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https://github.com/margaretmz/awesome-tensorflow-lite
An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources.
https://github.com/margaretmz/awesome-tensorflow-lite
List: awesome-tensorflow-lite
android awesome awesome-list computer-vision deep-learning flutter ios keras-tutorials mediapipe mlkit mobile model-zoo sample-app tensorflow tensorflow-keras tensorflow-lite tensorflow-models tfhub tflite tflite-models
Last synced: about 1 month ago
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An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources.
- Host: GitHub
- URL: https://github.com/margaretmz/awesome-tensorflow-lite
- Owner: margaretmz
- License: apache-2.0
- Created: 2020-01-29T15:15:22.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2022-03-01T02:42:46.000Z (over 2 years ago)
- Last Synced: 2024-07-31T04:02:01.593Z (3 months ago)
- Topics: android, awesome, awesome-list, computer-vision, deep-learning, flutter, ios, keras-tutorials, mediapipe, mlkit, mobile, model-zoo, sample-app, tensorflow, tensorflow-keras, tensorflow-lite, tensorflow-models, tfhub, tflite, tflite-models
- Homepage:
- Size: 11.7 MB
- Stars: 1,151
- Watchers: 61
- Forks: 176
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Awesome TensorFlow Lite [](https://awesome.re) [](http://makeapullrequest.com) [](https://twitter.com/margaretmz)
[TensorFlow Lite](https://www.tensorflow.org/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](CONTRIBUTING.md).
## Contents
- [Past announcements:](#past-announcements)
- [Models with samples](#models-with-samples)
- [Computer vision](#computer-vision)
- [Classification](#classification)
- [Detection](#detection)
- [Segmentation](#segmentation)
- [Style Transfer](#style-transfer)
- [Generative](#generative)
- [Post estimation](#post-estimation)
- [Other](#other)
- [Text](#text)
- [Speech](#speech)
- [Recommendation](#recommendation)
- [Game](#game)
- [Model zoo](#model-zoo)
- [TensorFlow Lite models](#tensorflow-lite-models)
- [TensorFlow models](#tensorflow-models)
- [Ideas and Inspiration](#ideas-and-inspiration)
- [ML Kit examples](#ml-kit-examples)
- [Plugins and SDKs](#plugins-and-sdks)
- [Helpful links](#helpful-links)
- [Learning resources](#learning-resources)
- [Blog posts](#blog-posts)
- [Books](#books)
- [Videos](#videos)
- [Podcasts](#podcasts)
- [MOOCs](#moocs)
## Past announcements:
Here are some past feature annoucements of TensorFlow Lite:
* [Announcement of the new converter](https://groups.google.com/a/tensorflow.org/d/msg/tflite/Z_h7706dt8Q/sNrjPj4yGgAJ) - [MLIR](https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d)-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](https://github.com/tensorflow/tflite-support/tree/master/tensorflow_lite_support/java) - Makes mobile development easier ([Android](https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/android/EXPLORE_THE_CODE.md) sample code).
* [Model Maker](https://www.tensorflow.org/lite/guide/model_maker) - Create your custom [image & text](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker) classification models easily in a few lines of code. See below the Icon Classifier for a tutorial by the community.
* [On-device training](https://blog.tensorflow.org/2019/12/example-on-device-model-personalization.html) - It is finally here! Currently limited to transfer learning for image classification only but it's a great start. See the official [Android](https://github.com/tensorflow/examples/blob/master/lite/examples/model_personalization/README.md) sample code and another one from the community ([Blog](https://aqibsaeed.github.io/on-device-activity-recognition) | [Android](https://github.com/aqibsaeed/on-device-activity-recognition)).
* [Hexagon delegate](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/hexagon_delegate.md) - 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](https://blog.tensorflow.org/2019/12/accelerating-tensorflow-lite-on-qualcomm.html).
* [Model Metadata](https://www.tensorflow.org/lite/convert/metadata) - Provides a standard for model descriptions which also enables [Code Gen and Android Studio ML Model Binding](https://www.tensorflow.org/lite/inference_with_metadata/codegen).
## 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](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip)) | [Android](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android) \| [iOS](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios) \| [Raspberry Pi](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/raspberry_pi) \| [Overview](https://www.tensorflow.org/lite/models/image_classification/overview) | tensorflow.org |
| Classification | MobileNetV2 | Recognize Flowers on Android [Codelab](https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0) \| [Android](https://github.com/tensorflow/examples/tree/master/lite/codelabs/flower_classification/android) | TensorFlow team |
| Classification | MobileNetV2 | Skin Lesion Detection [Android](https://github.com/AakashKumarNain/skin_cancer_detection/tree/master/demo) | Community |
| Classification | MobileNetV2 | American Sign Language Detection \| [Colab Notebook](https://colab.research.google.com/drive/1xsunX7Qj_XWBZwcZLyjsKBg4RI0DNo2-?usp=sharing) \| [Android](https://github.com/sayannath/American-Sign-Language-Detection) | Community |
| Classification | CNN + Quantisation Aware Training | Stone Paper Scissor Detection [Colab Notebook](https://colab.research.google.com/drive/1Wdso2N_76E8Xxniqd4C6T1sV5BuhKN1o?usp=sharing) \| [Flutter](https://github.com/sayannath/American-Sign-Language-Detection) | Community |
| Classification | EfficientNet-Lite0 ([download](https://github.com/margaretmz/icon-classifier/blob/master/ml-code/icons-50.tflite)) | Icon Classifier [Colab & Android](https://github.com/margaretmz/icon-classifier) \| [tutorial 1](https://medium.com/swlh/icon-classifier-with-tflite-model-maker-9263c0021f72) \| [tutorial 2](https://medium.com/@margaretmz/icon-classifier-android-app-1fc0b727f761) | Community |
#### Detection
| Task | Model | App \| Reference | Source |
| -|-|-|-|
| Object detection | Quantized COCO SSD MobileNet v1 ([download](https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip)) | [Android](https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android) \| [iOS](https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/ios) \| [Overview](https://www.tensorflow.org/lite/models/object_detection/overview#starter_model) | tensorflow.org |
| Object detection | YOLO | [Flutter](https://blog.francium.tech/real-time-object-detection-on-mobile-with-flutter-tensorflow-lite-and-yolo-android-part-a0042c9b62c6) \| [Paper](https://arxiv.org/abs/1506.02640) | Community |
| Object detection | [YOLOv5](https://tfhub.dev/neso613/lite-model/yolo-v5-tflite/tflite_model/1) | [Yolov5 Inference ](https://github.com/neso613/yolo-v5-tflite-model) | Community |
| Object detection | MobileNetV2 SSD ([download](https://github.com/google/mediapipe/tree/master/mediapipe/models/ssdlite_object_detection.tflite)) | [Reference](https://github.com/google/mediapipe/blob/master/mediapipe/models/object_detection_saved_model/README.md) | MediaPipe |
| Object detection | MobileDet ([Paper](https://arxiv.org/abs/2004.14525)) | [Blog post (includes the TFLite conversion process)](https://sayak.dev/mobiledet-optimization/) | MobileDet is from University of Wisconsin-Madison and Google and the blog post is from the Community |
| License Plate detection | SSD MobileNet [(download)](https://github.com/ariG23498/Flutter-License/blob/master/assets/detect.tflite) | [Flutter](https://github.com/ariG23498/Flutter-License) | Community |
| Face detection | BlazeFace ([download](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite)) | [Paper](https://sites.google.com/corp/view/perception-cv4arvr/blazeface) | MediaPipe |
| Face Authentication | [FaceNet](https://arxiv.org/pdf/1503.03832.pdf) | [Flutter](https://github.com/sayannath/Face-Authentication-App) | Community |
| Hand detection & tracking | Palm detection & hand landmarks ([download](https://github.com/google/mediapipe/tree/master/mediapipe/models#hand-detection-and-tracking)) | [Blog post](https://mediapipe.page.link/handgoogleaiblog) \| [Model card](https://mediapipe.page.link/handmc) \| [Android](https://github.com/supremetech/mediapipe-demo-hand-detection) | MediaPipe & Community |
#### Segmentation
| Task | Model | App \| Reference | Source |
| -|-|-|-|
| Segmentation | DeepLab V3 ([download](https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/deeplabv3_257_mv_gpu.tflite)) | [Android & iOS](https://github.com/tensorflow/examples/tree/master/lite/examples/image_segmentation/) \| [Overview](https://www.tensorflow.org/lite/models/segmentation/overview) \| Flutter [Image](https://github.com/kshitizrimal/Flutter-TFLite-Image-Segmentation) \| [Realtime](https://github.com/kshitizrimal/tflite-realtime-flutter) \| [Paper](https://arxiv.org/abs/1706.05587) | tf.org & Community |
| Segmentation | Different variants of [DeepLab V3 models](https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md) | Models on [TF Hub](https://tfhub.dev/s?module-type=image-segmentation&publisher=sayakpaul) with Colab Notebooks | Community |
| Segmentation | [DeepLab V3 model](https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/2?lite-format=tflite) | [Android](https://github.com/farmaker47/Update_image_segmentation) \| [Tutorial](https://farmaker47.medium.com/use-camerax-with-image-segmentation-android-project-d8656f35cea3) | Community |
| Hair Segmentation | [Download](https://github.com/google/mediapipe/tree/master/mediapipe/models/hair_segmentation.tflite) | [Paper](https://sites.google.com/corp/view/perception-cv4arvr/hair-segmentation) | MediaPipe |
#### Style Transfer
| Task | Model | App \| Reference | Source |
| -|-|-|-|
| Style transfer | [Arbitrary image stylization](https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization) | [Overview](https://www.tensorflow.org/lite/models/style_transfer/overview) \| [Android](https://github.com/tensorflow/examples/tree/master/lite/examples/style_transfer/android) \| [Flutter](https://github.com/PuzzleLeaf/flutter_tflite_style_transfer) | tf.org & Community |
| Style transfer | Better-quality style transfer models in .tflite | Models on [TF Hub](https://tfhub.dev/sayakpaul/lite-model/arbitrary-image-stylization-inceptionv3/dr/predict/1) with Colab Notebooks | Community |
| Video Style Transfer | Download:
[Dynamic range models](https://tfhub.dev/sayakpaul/lite-model/arbitrary-image-stylization-inceptionv3-dynamic-shapes/dr/transfer/1)) | [Android](https://github.com/farmaker47/video_style_transfer) \| [Tutorial](https://medium.com/@farmaker47/android-implementation-of-video-style-transfer-with-tensorflow-lite-models-9338a6d2a3ea) | Community |
| Segmentation & Style transfer | DeepLabV3 & Style Transfer [models](https://github.com/margaretmz/segmentation-style-transfer/tree/master/ml) | [Project repo](https://github.com/margaretmz/segmentation-style-transfer) \| [Android](https://github.com/margaretmz/segmentation-style-transfer/tree/master/android) \| [Tutorial](https://medium.com/google-developer-experts/image-background-stylizer-part-1-project-intro-d68c4547e7e3) | Community |
#### Generative
| Task | Model | App \| Reference | Source |
| -|-|-|-|
| GANs | [U-GAT-IT](https://github.com/taki0112/UGATIT) (Selfie2Anime) | [Project repo](https://github.com/margaretmz/selfie2anime-with-tflite) \| [Android](https://github.com/margaretmz/selfie2anime-with-tflite/tree/master/android) \| [Tutorial](https://medium.com/google-developer-experts/selfie2anime-with-tflite-part-1-overview-f97500800ffe) | Community |
| GANs | [White-box CartoonGAN](https://github.com/SystemErrorWang/White-box-Cartoonization) ([download](https://tfhub.dev/sayakpaul/lite-model/cartoongan/dr/1)) | [Project repo](https://github.com/margaretmz/Cartoonizer-with-TFLite) \| [Android](https://github.com/margaretmz/Cartoonizer-with-TFLite/tree/master/android) \| [Tutorial](https://blog.tensorflow.org/2020/09/how-to-create-cartoonizer-with-tf-lite.html) | Community |
| GANs - Image Extrapolation | Boundless on [TF Hub](https://tfhub.dev/sayakpaul/lite-model/boundless-quarter/dr/1) | [Colab Notebook](https://colab.research.google.com/github/sayakpaul/Adventures-in-TensorFlow-Lite/blob/master/Boundless_TFLite.ipynb) \| [Original Paper](https://arxiv.org/pdf/2003.06792v2.pdf) | Community |
#### Post estimation
| Task | Model | App \| Reference | Source |
| -|-|-|-|
| Pose estimation | Posenet ([download](https://storage.googleapis.com/download.tensorflow.org/models/tflite/posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite)) | [Android](https://github.com/tensorflow/examples/tree/master/lite/examples/posenet/android) \| [iOS](https://github.com/tensorflow/examples/tree/master/lite/examples/posenet/ios) \| [Overview](https://www.tensorflow.org/lite/models/pose_estimation/overview) | tensorflow.org |
| Pose Classification based Video Game Control | MoveNet Lightning ([download](https://github.com/NSTiwari/Video-Game-Control-using-Pose-Classification-and-TensorFlow-Lite/blob/main/movenet_lightning.tflite)) | [Project Repository](https://github.com/NSTiwari/Video-Game-Control-using-Pose-Classification-and-TensorFlow-Lite) | Community |
#### Other
| Task | Model | App \| Reference | Source |
| -|-|-|-|
| Low-light image enhancement | [Models on TF Hub](https://tfhub.dev/sayakpaul/mirnet-fixed/1) | [Project repo](https://github.com/sayakpaul/MIRNet-TFLite) \| [Original Paper](https://arxiv.org/pdf/2003.06792v2.pdf) \| [Flutter](https://github.com/sayannath/MIRNet-Flutter)| | Community |
| OCR |[Models on TF Hub](https://tfhub.dev/tulasiram58827/lite-model/keras-ocr/dr/2) | [Project Repository](https://github.com/tulasiram58827/ocr_tflite) | Community
### Text
| Task | Model | Sample apps | Source |
| ------------------- |---------------------------------------------------------------------------------------------------------------------------------| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ |
| Question & Answer | DistilBERT | [Android](https://github.com/huggingface/tflite-android-transformers/blob/master/bert) | Hugging Face |
| Text Generation | GPT-2 / DistilGPT2 | [Android](https://github.com/huggingface/tflite-android-transformers/blob/master/gpt2) | Hugging Face |
| Text Classification | [Download](https://storage.googleapis.com/download.tensorflow.org/models/tflite/text_classification/text_classification.tflite) | [Android](https://github.com/tensorflow/examples/tree/master/lite/examples/text_classification/android) \|[iOS](https://github.com/khurram18/TextClassafication) \| [Flutter](https://github.com/am15h/tflite_flutter_plugin/tree/master/example) | tf.org & Community |
| Text Detection | CRAFT Text Detector ([Paper](https://arxiv.org/pdf/1904.01941)) |[Download](https://github.com/tulasiram58827/craft_tflite/blob/main/models/craft_float_800.tflite?raw=true) \| [Project Repository](https://github.com/tulasiram58827/craft_tflite/) \| [Blog1-Conversion to TFLite](https://tulasi.dev/craft-in-tflite) \| [Blog2-EAST vs CRAFT](https://sayak.dev/optimizing-text-detectors/) \| [Models on TF Hub](https://tfhub.dev/tulasiram58827/lite-model/craft-text-detector/dr/1) \| Android (Coming Soon) | Community |
| Text Detection | EAST Text Detector ([Paper](https://arxiv.org/abs/1704.03155)) |[Models on TF Hub](https://tfhub.dev/sayakpaul/lite-model/east-text-detector/dr/1) \| [Conversion and Inference Notebook](https://colab.research.google.com/github/sayakpaul/Adventures-in-TensorFlow-Lite/blob/master/EAST_TFLite.ipynb) | Community |
### Speech
| Task | Model | App \| Reference | Source |
| ------------------ |------------------------------------| ------------------------------------------------------------------------------------- | ------------ |
| Speech Recognition | DeepSpeech | [Reference](https://github.com/mozilla/DeepSpeech/tree/master/native_client/java) | Mozilla |
| Speech Recognition | CONFORMER | [Inference](https://github.com/neso613/ASR_TFLite) [Android](https://github.com/windmaple/tflite-asr) | Community |
| Speech Synthesis | Tacotron-2, FastSpeech2, MB-Melgan | [Android](https://github.com/TensorSpeech/TensorflowTTS/tree/master/examples/android) | TensorSpeech |
| Speech Synthesis(TTS) | Tacotron2, FastSpeech2, MelGAN, MB-MelGAN, HiFi-GAN, Parallel WaveGAN | [Inference Notebook](https://github.com/tulasiram58827/TTS_TFLite/blob/main/End_to_End_TTS.ipynb) \| [Project Repository](https://github.com/tulasiram58827/TTS_TFLite/) | Community |
### Recommendation
| Task | Model | App \| Reference | Source |
| ------------------ |------------------------------------| ------------------------------------------------------------------------------------- | ------------ |
| On-device Recommendation | [Dual-Encoder](https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/ml) | [Android](https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/android) \| [iOS](https://github.com/zhuzilin/on-device_recommendation_tflite) \| [Reference](https://blog.tensorflow.org/2020/09/introduction-to-tflite-on-device-recommendation.html) | tf.org & Community |
### Game
| Task | Model | App \| Reference | Source |
| ------------------ |------------------------------------| ------------------------------------------------------------------------------------- | ------------ |
| Game agent | Reinforcement learning | [Flutter](https://github.com/windmaple/planestrike-flutter) \| [Tutorial](https://windmaple.medium.com/) | Community |
## Model zoo
### TensorFlow Lite models
These are the TensorFlow Lite models that could be implemented in apps and things:
* [MobileNet](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/README.md) - Pretrained MobileNet v2 and v3 models.
* TensorFlow Lite models
* [TensorFlow Lite models](https://www.tensorflow.org/lite/models) - With official Android and iOS examples.
* [Pretrained models](https://www.tensorflow.org/lite/guide/hosted_models) - Quantized and floating point variants.
* [TensorFlow Hub](https://tfhub.dev/) - 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](https://github.com/tensorflow/models/tree/master/official) - Official TensorFlow models.
* [Tensorflow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md) - Pre-trained on COCO, KITTI, AVA v2.1, iNaturalist Species datasets.
## Ideas and Inspiration
* [E2E TFLite Tutorials](https://github.com/ml-gde/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](https://developers.google.com/ml-kit) is a mobile SDK that brings Google's ML expertise to mobile developers.
* 2019-10-01 [ML Kit Translate demo](https://codelabs.developers.google.com/codelabs/mlkit-android-translate/#0) - A tutorial with material design [Android](https://github.com/googlecodelabs/mlkit-android/tree/master/translate) (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](https://youtu.be/ymyYUCrJnxU).
* 2019-02-09 [Flutter + MLKit: Business Card Mail Extractor](https://medium.com/flutter-community/flutter-mlkit-8039ec66b6a) - A blog post with a [Flutter](https://github.com/DaemonLoki/Business-Card-Mail-Extractor) sample code.
* 2019-02-08 [From TensorFlow to ML Kit: Power your Android application with machine learning](https://speakerdeck.com/jinqian/from-tensorflow-to-ml-kit-power-your-android-application-with-machine-learning) - A talk with [Android](https://github.com/xebia-france/magritte) (Kotlin) sample code.
* 2018-08-07 [Building a Custom Machine Learning Model on Android with TensorFlow Lite](https://medium.com/over-engineering/building-a-custom-machine-learning-model-on-android-with-tensorflow-lite-26447e53abf2).
* 2018-07-20 [ML Kit and Face Detection in Flutter](https://flatteredwithflutter.com/ml-kit-and-face-detection-in-flutter/).
* 2018-07-27 [ML Kit on Android 4: Landmark Detection](https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-landmark-detection-part-four-5e86b8deac3a).
* 2018-07-28 [ML Kit on Android 3: Barcode Scanning](https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-barcode-scanning-part-three-cc6f5921a108).
* 2018-05-31 [ML Kit on Android 2: Face Detection](https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-face-detection-part-two-de7e307c52e0).
* 2018-05-22 [ML Kit on Android 1: Intro](https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-introducing-mlkit-part-one-98fcfedbeee0).
## Plugins and SDKs
* [Edge Impulse](https://www.edgeimpulse.com/) - Created by [@EdgeImpulse](https://twitter.com/EdgeImpulse) to help you to train TensorFlow Lite models for embedded devices in the cloud.
* [MediaPipe](https://github.com/google/mediapipe) - A cross platform (mobile, desktop and Edge TPUs) AI pipeline by Google AI. (PM [Ming Yong](https://twitter.com/realmgyong)) | [MediaPipe examples](https://mediapipe.readthedocs.io/en/latest/examples.html).
* [Coral Edge TPU](https://coral.ai/) - Edge hardware by Google. [Coral Edge TPU examples](https://coral.ai/examples/).
* [TensorFlow Lite Flutter Plugin](https://github.com/am15h/tflite_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](https://pub.dev/packages/tflite_flutter).
## Helpful links
* [Netron](https://github.com/lutzroeder/netron) - A tool for visualizing models.
* [AI benchmark](http://ai-benchmark.com/tests.html) - A website for benchmarking computer vision models on smartphones.
* [Performance measurement](https://www.tensorflow.org/lite/performance/measurement) - How to measure model performance on Android and iOS.
* [Material design guidelines for ML](https://material.io/collections/machine-learning/patterns-for-machine-learning-powered-features.html) - How to design machine learning powered features. A good example: [ML Kit Showcase App](https://github.com/firebase/mlkit-material-android).
* [The People + AI Guide book](https://pair.withgoogle.com/) - Learn how to design human-centered AI products.
* [Adventures in TensorFlow Lite](https://github.com/sayakpaul/Adventures-in-TensorFlow-Lite) - A repository showing non-trivial conversion processes and general explorations in TensorFlow Lite.
* [TFProfiler](https://github.com/iglaweb/TFProfiler) - An Android-based app to profile TensorFlow Lite models and measure its performance on smartphone.
* [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers)
* [TensorFlow Lite Examples - Android](https://github.com/dailystudio/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](https://github.com/SunitRoy2703/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](https://www.tensorflow.org/lite/examples)
## 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](https://blog.tensorflow.org/2021/11/on-device-training-in-tensorflow-lite.html)
* 2021-09-27 [Optical character recognition with TensorFlow Lite: A new example app](https://blog.tensorflow.org/2021/09/blog.tensorflow.org202109optical-character-recognition.html)
* 2021-06-16 [https://blog.tensorflow.org/2021/06/easier-object-detection-on-mobile-with-tf-lite.html](https://blog.tensorflow.org/2021/11/on-device-training-in-tensorflow-lite.html)
* 2020-12-29 [YOLOv3 to TensorFlow Lite Conversion](https://medium.com/analytics-vidhya/yolov3-to-tensorflow-lite-conversion-4602cec5c239) - By Nitin Tiwari.
* 2020-04-20 [What is new in TensorFlow Lite](https://blog.tensorflow.org/2020/04/whats-new-in-tensorflow-lite-from-devsummit-2020.html) - By Khanh LeViet.
* 2020-04-17 [Optimizing style transfer to run on mobile with TFLite](https://blog.tensorflow.org/2020/04/optimizing-style-transfer-to-run-on-mobile-with-tflite.html) - By Khanh LeViet and Luiz Gustavo Martins.
* 2020-04-14 [How TensorFlow Lite helps you from prototype to product](https://blog.tensorflow.org/2020/04/how-tensorflow-lite-helps-you-from-prototype-to-product.html) - By Khanh LeViet.
* 2019-11-08 [Getting Started with ML on MCUs with TensorFlow](https://blog.particle.io/2019/11/08/particle-machine-learning-101/) - By Brandon Satrom.
* 2019-08-05 [TensorFlow Model Optimization Toolkit — float16 quantization halves model size](https://blog.tensorflow.org/2019/08/tensorflow-model-optimization-toolkit_5.html) - By the TensorFlow team.
* 2018-07-13 [Training and serving a real-time mobile object detector in 30 minutes with Cloud TPUs](https://blog.tensorflow.org/2018/07/training-and-serving-realtime-mobile-object-detector-cloud-tpus.html) - By Sara Robinson, Aakanksha Chowdhery, and Jonathan Huang.
* 2018-06-11 - [Why the Future of Machine Learning is Tiny](https://petewarden.com/2018/06/11/why-the-future-of-machine-learning-is-tiny/) - By Pete Warden.
* 2018-03-30 - [Using TensorFlow Lite on Android](https://blog.tensorflow.org/2018/03/using-tensorflow-lite-on-android.html)) - By Laurence Moroney.
### Books
* 2021-12-01 [AI and Machine Learning On-Device Development](https://learning.oreilly.com/library/view/ai-and-machine/9781098101732/) (early access) - By Laurence Moroney ([@lmoroney](https://twitter.com/lmoroney)).
* 2020-10-01 [AI and Machine Learning for Coders](https://learning.oreilly.com/library/view/ai-and-machine/9781492078180/) - By Laurence Moroney ([@lmoroney](https://twitter.com/lmoroney)).
* 2020-04-06 [Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter](https://www.packtpub.com/product/mobile-deep-learning-with-tensorflow-lite-ml-kit-and-flutter/9781789611212): Build scalable real-world projects to implement end-to-end neural networks on Android and iOS ([GitHub](https://github.com/PacktPublishing/Mobile-Deep-Learning-Projects)) - By Anubhav Singh ([@xprilion](https://github.com/xprilion)) and Rimjhim Bhadani ([@Rimjhim28](https://github.com/Rimjhim28)).
* 2020-03-01 Raspberry Pi for Computer Vision ([Complete Bundle](https://www.pyimagesearch.com/raspberry-pi-for-computer-vision) | [TOC](https://www.pyimagesearch.com/2019/04/05/table-of-contents-raspberry-pi-for-computer-vision/)) - By the PyImageSearch Team: Adrian Rosebrock ([@PyImageSearch](https://twitter.com/PyImageSearch)), David Hoffman, Asbhishek Thanki, Sayak Paul ([@RisingSayak](https://twitter.com/RisingSayak)), and David Mcduffee.
* 2019-12-01 [TinyML](http://shop.oreilly.com/product/0636920254508.do) - By Pete Warden ([@petewarden](https://twitter.com/petewarden)) and Daniel Situnayake ([@dansitu](https://twitter.com/dansitu)).
* 2019-10-01 [Practical Deep Learning for Cloud, Mobile, and Edge](https://www.practicaldeeplearning.ai/) - By Anirudh Koul ([@AnirudhKoul](https://twitter.com/AnirudhKoul)), Siddha Ganju ([@SiddhaGanju](https://twitter.com/SiddhaGanju)), and Meher Kasam ([@MeherKasam](https://twitter.com/MeherKasam)).
### Videos
* 2021-10-06 [Contributing to TensorFlow Lite with Sunit Roy](https://youtu.be/sZayUoWW6nE) (Hacktoberfest 2021)
* 2020-07-25 [Android ML by Hoi Lam](https://youtu.be/m_bEh8YifnQ) (GDG Kolkata meetup).
* 2020-04-01 [Easy on-device ML from prototype to production](https://youtu.be/ALxWJoh_BHw) (TF Dev Summit 2020).
* 2020-03-11 [TensorFlow Lite: ML for mobile and IoT devices](https://youtu.be/27Zx-4GOQA8) (TF Dev Summit 2020).
* 2019-10-31 [Keynote - TensorFlow Lite: ML for mobile and IoT devices](https://youtu.be/zjDGAiLqGk8).
* 2019-10-31 [TensorFlow Lite: Solution for running ML on-device](https://youtu.be/0SpZy7iouFU).
* 2019-10-31 [TensorFlow model optimization: Quantization and pruning](https://youtu.be/3JWRVx1OKQQ).
* 2019-10-29 [Inside TensorFlow: TensorFlow Lite](https://youtu.be/gHN0jDbJz8E).
* 2018-04-18 [TensorFlow Lite for Android (Coding TensorFlow)](https://youtu.be/JnhW5tQ_7Vo).
### Podcasts
* 2020-08-08 [Talking Machine Learning with Hoi Lam](https://anchor.fm/talkingwithapples/episodes/Talking-Machine-Learning-with-Hoi-Lam-eiaj7v).
### MOOCs
* [Introduction to TensorFlow Lite](https://www.udacity.com/course/intro-to-tensorflow-lite--ud190) - Udacity course by Daniel Situnayake (@dansitu), Paige Bailey ([@DynamicWebPaige](https://twitter.com/DynamicWebPaige)), and Juan Delgado.
* [Device-based Models with TensorFlow Lite](https://www.coursera.org/learn/device-based-models-tensorflow) - Coursera course by Laurence Moroney ([@lmoroney](https://twitter.com/lmoroney)).
* [The Future of ML is Tiny and Bright](https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning) - A series of edX courses created by Harvard in collaboration with Google. Instructors - Vijay Janapa Reddi, Laurence Moroney, and Pete Warden.