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--- visibility: public --- # TensorFlow Lite **repo:** [margaretmz/awesome-tensorflow-lite](https://github.com/margaretmz/awesome-tensorflow-lite) **category:** [[computer-science|Computer Science]] **related:** [[tensorflow|Tensorflow]] --- # Awesome TensorFlow Lite [](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](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite * Put all the samples side-by-side for easy reference * Share knowledge and [learning](/@harrisonqian/awesome/wiki/programming-languages/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](/@harrisonqian/awesome/wiki/computer-science/tensorflow)-lite-models) - [TensorFlow models](#[tensorflow](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/programming-languages/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/2019/12/example-on-device-model-personalization.html) - It is finally here! Currently limited to transfer [learning](/@harrisonqian/awesome/wiki/programming-languages/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](/@harrisonqian/awesome/wiki/development-environment/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](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite on Qualcomm Hexagon DSPs](https://blog.tensorflow.org/2019/12/accelerating-tensorflow-lite-on-qualcomm.html). * [Model Metadata](https://www.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/lite/convert/metadata) - Provides a standard for model descriptions which also enables [Code Gen and [Android](/@harrisonqian/awesome/wiki/platforms/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: <br> [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](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite models * [TensorFlow Lite models](https://www.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/lite/models) - With official [Android](/@harrisonqian/awesome/wiki/platforms/android) and iOS examples. * [Pretrained models](https://www.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/lite/guide/hosted_models) - Quantized and floating point variants. * [TensorFlow Hub](https://tfhub.dev/) - Set "Model format = TFLite" to find [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/miscellaneous/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](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite model(s), sample code and tutorial will be added to this [awesome](/@harrisonqian/awesome/wiki/miscellaneous/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](/@harrisonqian/awesome/wiki/platforms/android)-translate/#0) - A tutorial with [material design](/@harrisonqian/awesome/wiki/front-end-development/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](/@harrisonqian/awesome/wiki/platforms/firebase). * 2019-03-13 [Computer Vision with ML Kit - [Flutter](/@harrisonqian/awesome/wiki/platforms/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](/@harrisonqian/awesome/wiki/computer-science/tensorflow) to ML Kit: Power your [Android](/@harrisonqian/awesome/wiki/platforms/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](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Model on [Android](/@harrisonqian/awesome/wiki/platforms/android) with [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite](https://medium.com/over-engineering/building-a-custom-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/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](/@harrisonqian/awesome/wiki/platforms/android) 4: Landmark Detection](https://medium.com/google-developer-experts/exploring-[firebase](/@harrisonqian/awesome/wiki/platforms/firebase)-mlkit-on-android-landmark-detection-part-four-5e86b8deac3a). * 2018-07-28 [ML Kit on [Android](/@harrisonqian/awesome/wiki/platforms/android) 3: Barcode Scanning](https://medium.com/google-developer-experts/exploring-[firebase](/@harrisonqian/awesome/wiki/platforms/firebase)-mlkit-on-android-barcode-scanning-part-three-cc6f5921a108). * 2018-05-31 [ML Kit on [Android](/@harrisonqian/awesome/wiki/platforms/android) 2: Face Detection](https://medium.com/google-developer-experts/exploring-[firebase](/@harrisonqian/awesome/wiki/platforms/firebase)-mlkit-on-android-face-detection-part-two-de7e307c52e0). * 2018-05-22 [ML Kit on [Android](/@harrisonqian/awesome/wiki/platforms/android) 1: Intro](https://medium.com/google-developer-experts/exploring-[firebase](/@harrisonqian/awesome/wiki/platforms/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/platforms/flutter) Plugin](https://github.com/am15h/tflite_flutter_plugin/) - Provides a [dart](/@harrisonqian/awesome/wiki/programming-languages/dart) API similar to the [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite [Java](/@harrisonqian/awesome/wiki/programming-languages/java) API for accessing TensorFlow Lite interpreter and performing inference in [flutter](/@harrisonqian/awesome/wiki/platforms/flutter) [apps](/@harrisonqian/awesome/wiki/platforms/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](/@harrisonqian/awesome/wiki/computer-science/computer-vision) models on smartphones. * [Performance measurement](https://www.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/lite/performance/measurement) - How to measure model performance on [Android](/@harrisonqian/awesome/wiki/platforms/android) and iOS. * [Material design guidelines for ML](https://material.io/collections/machine-learning/patterns-for-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-powered-features.html) - How to design [machine learning](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/platforms/android)-based app to profile [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite models and measure its performance on smartphone. * [TensorFlow Lite for Microcontrollers](https://www.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/lite/microcontrollers) * [TensorFlow Lite Examples - Android](https://github.com/dailystudio/tensorflow-lite-examples-android) - A repository refactors and rewrites all the [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite [Android](/@harrisonqian/awesome/wiki/platforms/android) examples which are included in the [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) official website. * [Tensorflow-lite-[kotlin](/@harrisonqian/awesome/wiki/programming-languages/kotlin)-samples](https://github.com/SunitRoy2703/Tensorflow-lite-kotlin-samples) - A collection of [Tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite [Android](/@harrisonqian/awesome/wiki/platforms/android) example [Apps](/@harrisonqian/awesome/wiki/platforms/apps) in [Kotlin](/@harrisonqian/awesome/wiki/programming-languages/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](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite](https://blog.tensorflow.org/2021/11/on-device-training-in-tensorflow-lite.html) * 2021-09-27 [Optical character recognition with [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite: A new example app](https://blog.tensorflow.org/2021/09/blog.tensorflow.org202109optical-character-recognition.html) * 2021-06-16 [https://blog.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/programming-languages/learning)-101/) - By Brandon Satrom. * 2019-08-05 [TensorFlow Model Optimization Toolkit — float16 quantization halves model size](https://blog.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/machine-learning) is Tiny](https://petewarden.com/2018/06/11/why-the-future-of-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-is-tiny/) - By Pete Warden. * 2018-03-30 - [Using [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite on Android](https://blog.tensorflow.org/2018/03/using-tensorflow-lite-on-[android](/@harrisonqian/awesome/wiki/platforms/android).html)) - By Laurence Moroney. ### Books * 2021-12-01 [AI and [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/deep-learning) with [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) Lite, ML Kit and Flutter](https://www.packtpub.com/product/mobile-deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-with-tensorflow-lite-ml-kit-and-flutter/9781789611212): Build scalable real-world projects to implement end-to-end neural networks on [Android](/@harrisonqian/awesome/wiki/platforms/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](/@harrisonqian/awesome/wiki/platforms/raspberry-pi) for [Computer Vision](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/tensorflow): TensorFlow Lite](https://youtu.be/gHN0jDbJz8E). * 2018-04-18 [TensorFlow Lite for [Android](/@harrisonqian/awesome/wiki/platforms/android) (Coding TensorFlow)](https://youtu.be/JnhW5tQ_7Vo). ### Podcasts * 2020-08-08 [Talking [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) with Hoi Lam](https://anchor.fm/talkingwithapples/episodes/Talking-Machine-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-with-Hoi-Lam-eiaj7v). ### MOOCs * [Introduction to [TensorFlow](/@harrisonqian/awesome/wiki/computer-science/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](/@harrisonqian/awesome/wiki/computer-science/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.