rhetenor/awesome-awesomeness
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
Jonas Zeunert
2025-07-18 23:13:11 +02:00
parent c9485bf576
commit 652812eed0
2354 changed files with 1266414 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

970
html/tensorflowlite.md2.html Normal file
View File

@@ -0,0 +1,970 @@
<p align="center">
<img src="images/awesome-tflite.png" alt="awesome tflite" width="500">
</p>
<!-- omit in toc -->
<h1 id="awesome-tensorflow-lite-awesome-prs-welcome-twitter">Awesome
TensorFlow Lite <a href="https://awesome.re"><img
src="https://awesome.re/badge.svg" alt="Awesome" /></a> <a
href="http://makeapullrequest.com"><img
src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square"
alt="PRs Welcome" /></a> <a href="https://twitter.com/margaretmz"><img
src="https://img.shields.io/badge/Twitter-%40margaretmz-blue"
alt="Twitter" /></a></h1>
<p><a href="https://www.tensorflow.org/lite">TensorFlow Lite</a> 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.</p>
<p>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</p>
<p>Please submit a PR if you would like to contribute and follow the
guidelines <a href="CONTRIBUTING.md">here</a>.</p>
<!-- omit in toc -->
<p>## Contents - <a href="#past-announcements">Past announcements:</a> -
<a href="#models-with-samples">Models with samples</a> - <a
href="#computer-vision">Computer vision</a> - <a
href="#classification">Classification</a> - <a
href="#detection">Detection</a> - <a
href="#segmentation">Segmentation</a> - <a href="#style-transfer">Style
Transfer</a> - <a href="#generative">Generative</a> - <a
href="#post-estimation">Post estimation</a> - <a href="#other">Other</a>
- <a href="#text">Text</a> - <a href="#speech">Speech</a> - <a
href="#recommendation">Recommendation</a> - <a href="#game">Game</a> -
<a href="#model-zoo">Model zoo</a> - <a
href="#tensorflow-lite-models">TensorFlow Lite models</a> - <a
href="#tensorflow-models">TensorFlow models</a> - <a
href="#ideas-and-inspiration">Ideas and Inspiration</a> - <a
href="#ml-kit-examples">ML Kit examples</a> - <a
href="#plugins-and-sdks">Plugins and SDKs</a> - <a
href="#helpful-links">Helpful links</a> - <a
href="#learning-resources">Learning resources</a> - <a
href="#blog-posts">Blog posts</a> - <a href="#books">Books</a> - <a
href="#videos">Videos</a> - <a href="#podcasts">Podcasts</a> - <a
href="#moocs">MOOCs</a></p>
<h2 id="past-announcements">Past announcements:</h2>
<p>Here are some past feature annoucements of TensorFlow Lite: * <a
href="https://groups.google.com/a/tensorflow.org/d/msg/tflite/Z_h7706dt8Q/sNrjPj4yGgAJ">Announcement
of the new converter</a> - <a
href="https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d">MLIR</a>-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. *
<a
href="https://github.com/tensorflow/tflite-support/tree/master/tensorflow_lite_support/java">Android
Support Library</a> - Makes mobile development easier (<a
href="https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/android/EXPLORE_THE_CODE.md">Android</a>
sample code). * <a
href="https://www.tensorflow.org/lite/guide/model_maker">Model Maker</a>
- Create your custom <a
href="https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker">image
&amp; text</a> classification models easily in a few lines of code. See
below the Icon Classifier for a tutorial by the community. * <a
href="https://blog.tensorflow.org/2019/12/example-on-device-model-personalization.html">On-device
training</a> - It is finally here! Currently limited to transfer
learning for image classification only but it’s a great start. See the
official <a
href="https://github.com/tensorflow/examples/blob/master/lite/examples/model_personalization/README.md">Android</a>
sample code and another one from the community (<a
href="https://aqibsaeed.github.io/on-device-activity-recognition">Blog</a>
| <a
href="https://github.com/aqibsaeed/on-device-activity-recognition">Android</a>).
* <a
href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/hexagon_delegate.md">Hexagon
delegate</a> - How to use the Hexagon Delegate to speed up model
inference on mobile and edge devices. Also see blog post <a
href="https://blog.tensorflow.org/2019/12/accelerating-tensorflow-lite-on-qualcomm.html">Accelerating
TensorFlow Lite on Qualcomm Hexagon DSPs</a>. * <a
href="https://www.tensorflow.org/lite/convert/metadata">Model
Metadata</a> - Provides a standard for model descriptions which also
enables <a
href="https://www.tensorflow.org/lite/inference_with_metadata/codegen">Code
Gen and Android Studio ML Model Binding</a>.</p>
<h2 id="models-with-samples">Models with samples</h2>
<p>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.</p>
<h3 id="computer-vision">Computer vision</h3>
<h4 id="classification">Classification</h4>
<table>
<colgroup>
<col style="width: 5%" />
<col style="width: 26%" />
<col style="width: 65%" />
<col style="width: 3%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Classification</td>
<td>MobileNetV1 (<a
href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip">download</a>)</td>
<td><a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android">Android</a>
| <a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios">iOS</a>
| <a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/raspberry_pi">Raspberry
Pi</a> | <a
href="https://www.tensorflow.org/lite/models/image_classification/overview">Overview</a></td>
<td>tensorflow.org</td>
</tr>
<tr class="even">
<td>Classification</td>
<td>MobileNetV2</td>
<td>Recognize Flowers on Android <a
href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">Codelab</a>
| <a
href="https://github.com/tensorflow/examples/tree/master/lite/codelabs/flower_classification/android">Android</a></td>
<td>TensorFlow team</td>
</tr>
<tr class="odd">
<td>Classification</td>
<td>MobileNetV2</td>
<td>Skin Lesion Detection <a
href="https://github.com/AakashKumarNain/skin_cancer_detection/tree/master/demo">Android</a></td>
<td>Community</td>
</tr>
<tr class="even">
<td>Classification</td>
<td>MobileNetV2</td>
<td>American Sign Language Detection | <a
href="https://colab.research.google.com/drive/1xsunX7Qj_XWBZwcZLyjsKBg4RI0DNo2-?usp=sharing">Colab
Notebook</a> | <a
href="https://github.com/sayannath/American-Sign-Language-Detection">Android</a></td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Classification</td>
<td>CNN + Quantisation Aware Training</td>
<td>Stone Paper Scissor Detection <a
href="https://colab.research.google.com/drive/1Wdso2N_76E8Xxniqd4C6T1sV5BuhKN1o?usp=sharing">Colab
Notebook</a> | <a
href="https://github.com/sayannath/American-Sign-Language-Detection">Flutter</a></td>
<td>Community</td>
</tr>
<tr class="even">
<td>Classification</td>
<td>EfficientNet-Lite0 (<a
href="https://github.com/margaretmz/icon-classifier/blob/master/ml-code/icons-50.tflite">download</a>)</td>
<td>Icon Classifier <a
href="https://github.com/margaretmz/icon-classifier">Colab &amp;
Android</a> | <a
href="https://medium.com/swlh/icon-classifier-with-tflite-model-maker-9263c0021f72">tutorial
1</a> | <a
href="https://medium.com/@margaretmz/icon-classifier-android-app-1fc0b727f761">tutorial
2</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h4 id="detection">Detection</h4>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Object detection</td>
<td>Quantized COCO SSD MobileNet v1 (<a
href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">download</a>)</td>
<td><a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android">Android</a>
| <a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/ios">iOS</a>
| <a
href="https://www.tensorflow.org/lite/models/object_detection/overview#starter_model">Overview</a></td>
<td>tensorflow.org</td>
</tr>
<tr class="even">
<td>Object detection</td>
<td>YOLO</td>
<td><a
href="https://blog.francium.tech/real-time-object-detection-on-mobile-with-flutter-tensorflow-lite-and-yolo-android-part-a0042c9b62c6">Flutter</a>
| <a href="https://arxiv.org/abs/1506.02640">Paper</a></td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Object detection</td>
<td><a
href="https://tfhub.dev/neso613/lite-model/yolo-v5-tflite/tflite_model/1">YOLOv5</a></td>
<td><a href="https://github.com/neso613/yolo-v5-tflite-model">Yolov5
Inference</a></td>
<td>Community</td>
</tr>
<tr class="even">
<td>Object detection</td>
<td>MobileNetV2 SSD (<a
href="https://github.com/google/mediapipe/tree/master/mediapipe/models/ssdlite_object_detection.tflite">download</a>)</td>
<td><a
href="https://github.com/google/mediapipe/blob/master/mediapipe/models/object_detection_saved_model/README.md">Reference</a></td>
<td>MediaPipe</td>
</tr>
<tr class="odd">
<td>Object detection</td>
<td>MobileDet (<a
href="https://arxiv.org/abs/2004.14525">Paper</a>)</td>
<td><a href="https://sayak.dev/mobiledet-optimization/">Blog post
(includes the TFLite conversion process)</a></td>
<td>MobileDet is from University of Wisconsin-Madison and Google and the
blog post is from the Community</td>
</tr>
<tr class="even">
<td>License Plate detection</td>
<td>SSD MobileNet <a
href="https://github.com/ariG23498/Flutter-License/blob/master/assets/detect.tflite">(download)</a></td>
<td><a
href="https://github.com/ariG23498/Flutter-License">Flutter</a></td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Face detection</td>
<td>BlazeFace (<a
href="https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite">download</a>)</td>
<td><a
href="https://sites.google.com/corp/view/perception-cv4arvr/blazeface">Paper</a></td>
<td>MediaPipe</td>
</tr>
<tr class="even">
<td>Face Authentication</td>
<td><a href="https://arxiv.org/pdf/1503.03832.pdf">FaceNet</a></td>
<td><a
href="https://github.com/sayannath/Face-Authentication-App">Flutter</a></td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Hand detection &amp; tracking</td>
<td>Palm detection &amp; hand landmarks (<a
href="https://github.com/google/mediapipe/tree/master/mediapipe/models#hand-detection-and-tracking">download</a>)</td>
<td><a href="https://mediapipe.page.link/handgoogleaiblog">Blog post</a>
| <a href="https://mediapipe.page.link/handmc">Model card</a> | <a
href="https://github.com/supremetech/mediapipe-demo-hand-detection">Android</a></td>
<td>MediaPipe &amp; Community</td>
</tr>
</tbody>
</table>
<h4 id="segmentation">Segmentation</h4>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Segmentation</td>
<td>DeepLab V3 (<a
href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/deeplabv3_257_mv_gpu.tflite">download</a>)</td>
<td><a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_segmentation/">Android
&amp; iOS</a> | <a
href="https://www.tensorflow.org/lite/models/segmentation/overview">Overview</a>
| Flutter <a
href="https://github.com/kshitizrimal/Flutter-TFLite-Image-Segmentation">Image</a>
| <a
href="https://github.com/kshitizrimal/tflite-realtime-flutter">Realtime</a>
| <a href="https://arxiv.org/abs/1706.05587">Paper</a></td>
<td>tf.org &amp; Community</td>
</tr>
<tr class="even">
<td>Segmentation</td>
<td>Different variants of <a
href="https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md">DeepLab
V3 models</a></td>
<td>Models on <a
href="https://tfhub.dev/s?module-type=image-segmentation&amp;publisher=sayakpaul">TF
Hub</a> with Colab Notebooks</td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Segmentation</td>
<td><a
href="https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/2?lite-format=tflite">DeepLab
V3 model</a></td>
<td><a
href="https://github.com/farmaker47/Update_image_segmentation">Android</a>
| <a
href="https://farmaker47.medium.com/use-camerax-with-image-segmentation-android-project-d8656f35cea3">Tutorial</a></td>
<td>Community</td>
</tr>
<tr class="even">
<td>Hair Segmentation</td>
<td><a
href="https://github.com/google/mediapipe/tree/master/mediapipe/models/hair_segmentation.tflite">Download</a></td>
<td><a
href="https://sites.google.com/corp/view/perception-cv4arvr/hair-segmentation">Paper</a></td>
<td>MediaPipe</td>
</tr>
</tbody>
</table>
<h4 id="style-transfer">Style Transfer</h4>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Style transfer</td>
<td><a
href="https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization">Arbitrary
image stylization</a></td>
<td><a
href="https://www.tensorflow.org/lite/models/style_transfer/overview">Overview</a>
| <a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/style_transfer/android">Android</a>
| <a
href="https://github.com/PuzzleLeaf/flutter_tflite_style_transfer">Flutter</a></td>
<td>tf.org &amp; Community</td>
</tr>
<tr class="even">
<td>Style transfer</td>
<td>Better-quality style transfer models in .tflite</td>
<td>Models on <a
href="https://tfhub.dev/sayakpaul/lite-model/arbitrary-image-stylization-inceptionv3/dr/predict/1">TF
Hub</a> with Colab Notebooks</td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Video Style Transfer</td>
<td>Download: <br> <a
href="https://tfhub.dev/sayakpaul/lite-model/arbitrary-image-stylization-inceptionv3-dynamic-shapes/dr/transfer/1">Dynamic
range models</a>)</td>
<td><a
href="https://github.com/farmaker47/video_style_transfer">Android</a> |
<a
href="https://medium.com/@farmaker47/android-implementation-of-video-style-transfer-with-tensorflow-lite-models-9338a6d2a3ea">Tutorial</a></td>
<td>Community</td>
</tr>
<tr class="even">
<td>Segmentation &amp; Style transfer</td>
<td>DeepLabV3 &amp; Style Transfer <a
href="https://github.com/margaretmz/segmentation-style-transfer/tree/master/ml">models</a></td>
<td><a
href="https://github.com/margaretmz/segmentation-style-transfer">Project
repo</a> | <a
href="https://github.com/margaretmz/segmentation-style-transfer/tree/master/android">Android</a>
| <a
href="https://medium.com/google-developer-experts/image-background-stylizer-part-1-project-intro-d68c4547e7e3">Tutorial</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h4 id="generative">Generative</h4>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>GANs</td>
<td><a href="https://github.com/taki0112/UGATIT">U-GAT-IT</a>
(Selfie2Anime)</td>
<td><a
href="https://github.com/margaretmz/selfie2anime-with-tflite">Project
repo</a> | <a
href="https://github.com/margaretmz/selfie2anime-with-tflite/tree/master/android">Android</a>
| <a
href="https://medium.com/google-developer-experts/selfie2anime-with-tflite-part-1-overview-f97500800ffe">Tutorial</a></td>
<td>Community</td>
</tr>
<tr class="even">
<td>GANs</td>
<td><a
href="https://github.com/SystemErrorWang/White-box-Cartoonization">White-box
CartoonGAN</a> (<a
href="https://tfhub.dev/sayakpaul/lite-model/cartoongan/dr/1">download</a>)</td>
<td><a
href="https://github.com/margaretmz/Cartoonizer-with-TFLite">Project
repo</a> | <a
href="https://github.com/margaretmz/Cartoonizer-with-TFLite/tree/master/android">Android</a>
| <a
href="https://blog.tensorflow.org/2020/09/how-to-create-cartoonizer-with-tf-lite.html">Tutorial</a></td>
<td>Community</td>
</tr>
<tr class="odd">
<td>GANs - Image Extrapolation</td>
<td>Boundless on <a
href="https://tfhub.dev/sayakpaul/lite-model/boundless-quarter/dr/1">TF
Hub</a></td>
<td><a
href="https://colab.research.google.com/github/sayakpaul/Adventures-in-TensorFlow-Lite/blob/master/Boundless_TFLite.ipynb">Colab
Notebook</a> | <a href="https://arxiv.org/pdf/2003.06792v2.pdf">Original
Paper</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h4 id="post-estimation">Post estimation</h4>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Pose estimation</td>
<td>Posenet (<a
href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite">download</a>)</td>
<td><a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/posenet/android">Android</a>
| <a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/posenet/ios">iOS</a>
| <a
href="https://www.tensorflow.org/lite/models/pose_estimation/overview">Overview</a></td>
<td>tensorflow.org</td>
</tr>
<tr class="even">
<td>Pose Classification based Video Game Control</td>
<td>MoveNet Lightning (<a
href="https://github.com/NSTiwari/Video-Game-Control-using-Pose-Classification-and-TensorFlow-Lite/blob/main/movenet_lightning.tflite">download</a>)</td>
<td><a
href="https://github.com/NSTiwari/Video-Game-Control-using-Pose-Classification-and-TensorFlow-Lite">Project
Repository</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h4 id="other">Other</h4>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Low-light image enhancement</td>
<td><a href="https://tfhub.dev/sayakpaul/mirnet-fixed/1">Models on TF
Hub</a></td>
<td><a href="https://github.com/sayakpaul/MIRNet-TFLite">Project
repo</a> | <a href="https://arxiv.org/pdf/2003.06792v2.pdf">Original
Paper</a> | <a
href="https://github.com/sayannath/MIRNet-Flutter">Flutter</a></td>
<td></td>
</tr>
<tr class="even">
<td>OCR</td>
<td><a
href="https://tfhub.dev/tulasiram58827/lite-model/keras-ocr/dr/2">Models
on TF Hub</a></td>
<td><a href="https://github.com/tulasiram58827/ocr_tflite">Project
Repository</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h3 id="text">Text</h3>
<table>
<colgroup>
<col style="width: 4%" />
<col style="width: 31%" />
<col style="width: 59%" />
<col style="width: 4%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>Sample apps</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Question &amp; Answer</td>
<td>DistilBERT</td>
<td><a
href="https://github.com/huggingface/tflite-android-transformers/blob/master/bert">Android</a></td>
<td>Hugging Face</td>
</tr>
<tr class="even">
<td>Text Generation</td>
<td>GPT-2 / DistilGPT2</td>
<td><a
href="https://github.com/huggingface/tflite-android-transformers/blob/master/gpt2">Android</a></td>
<td>Hugging Face</td>
</tr>
<tr class="odd">
<td>Text Classification</td>
<td><a
href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/text_classification/text_classification.tflite">Download</a></td>
<td><a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/text_classification/android">Android</a>
|<a href="https://github.com/khurram18/TextClassafication">iOS</a> | <a
href="https://github.com/am15h/tflite_flutter_plugin/tree/master/example">Flutter</a></td>
<td>tf.org &amp; Community</td>
</tr>
<tr class="even">
<td>Text Detection</td>
<td>CRAFT Text Detector (<a
href="https://arxiv.org/pdf/1904.01941">Paper</a>)</td>
<td><a
href="https://github.com/tulasiram58827/craft_tflite/blob/main/models/craft_float_800.tflite?raw=true">Download</a>
| <a href="https://github.com/tulasiram58827/craft_tflite/">Project
Repository</a> | <a
href="https://tulasi.dev/craft-in-tflite">Blog1-Conversion to TFLite</a>
| <a href="https://sayak.dev/optimizing-text-detectors/">Blog2-EAST vs
CRAFT</a> | <a
href="https://tfhub.dev/tulasiram58827/lite-model/craft-text-detector/dr/1">Models
on TF Hub</a> | Android (Coming Soon)</td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Text Detection</td>
<td>EAST Text Detector (<a
href="https://arxiv.org/abs/1704.03155">Paper</a>)</td>
<td><a
href="https://tfhub.dev/sayakpaul/lite-model/east-text-detector/dr/1">Models
on TF Hub</a> | <a
href="https://colab.research.google.com/github/sayakpaul/Adventures-in-TensorFlow-Lite/blob/master/EAST_TFLite.ipynb">Conversion
and Inference Notebook</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h3 id="speech">Speech</h3>
<table>
<colgroup>
<col style="width: 11%" />
<col style="width: 23%" />
<col style="width: 56%" />
<col style="width: 7%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Speech Recognition</td>
<td>DeepSpeech</td>
<td><a
href="https://github.com/mozilla/DeepSpeech/tree/master/native_client/java">Reference</a></td>
<td>Mozilla</td>
</tr>
<tr class="even">
<td>Speech Recognition</td>
<td>CONFORMER</td>
<td><a href="https://github.com/neso613/ASR_TFLite">Inference</a> <a
href="https://github.com/windmaple/tflite-asr">Android</a></td>
<td>Community</td>
</tr>
<tr class="odd">
<td>Speech Synthesis</td>
<td>Tacotron-2, FastSpeech2, MB-Melgan</td>
<td><a
href="https://github.com/TensorSpeech/TensorflowTTS/tree/master/examples/android">Android</a></td>
<td>TensorSpeech</td>
</tr>
<tr class="even">
<td>Speech Synthesis(TTS)</td>
<td>Tacotron2, FastSpeech2, MelGAN, MB-MelGAN, HiFi-GAN, Parallel
WaveGAN</td>
<td><a
href="https://github.com/tulasiram58827/TTS_TFLite/blob/main/End_to_End_TTS.ipynb">Inference
Notebook</a> | <a
href="https://github.com/tulasiram58827/TTS_TFLite/">Project
Repository</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h3 id="recommendation">Recommendation</h3>
<table>
<colgroup>
<col style="width: 11%" />
<col style="width: 23%" />
<col style="width: 56%" />
<col style="width: 7%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>On-device Recommendation</td>
<td><a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/ml">Dual-Encoder</a></td>
<td><a
href="https://github.com/tensorflow/examples/tree/master/lite/examples/recommendation/android">Android</a>
| <a
href="https://github.com/zhuzilin/on-device_recommendation_tflite">iOS</a>
| <a
href="https://blog.tensorflow.org/2020/09/introduction-to-tflite-on-device-recommendation.html">Reference</a></td>
<td>tf.org &amp; Community</td>
</tr>
</tbody>
</table>
<h3 id="game">Game</h3>
<table>
<colgroup>
<col style="width: 11%" />
<col style="width: 23%" />
<col style="width: 56%" />
<col style="width: 7%" />
</colgroup>
<thead>
<tr class="header">
<th>Task</th>
<th>Model</th>
<th>App | Reference</th>
<th>Source</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Game agent</td>
<td>Reinforcement learning</td>
<td><a
href="https://github.com/windmaple/planestrike-flutter">Flutter</a> | <a
href="https://windmaple.medium.com/">Tutorial</a></td>
<td>Community</td>
</tr>
</tbody>
</table>
<h2 id="model-zoo">Model zoo</h2>
<h3 id="tensorflow-lite-models">TensorFlow Lite models</h3>
<p>These are the TensorFlow Lite models that could be implemented in
apps and things: * <a
href="https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/README.md">MobileNet</a>
- Pretrained MobileNet v2 and v3 models. * TensorFlow Lite models * <a
href="https://www.tensorflow.org/lite/models">TensorFlow Lite models</a>
- With official Android and iOS examples. * <a
href="https://www.tensorflow.org/lite/guide/hosted_models">Pretrained
models</a> - Quantized and floating point variants. * <a
href="https://tfhub.dev/">TensorFlow Hub</a> - Set “Model format =
TFLite” to find TensorFlow Lite models.</p>
<h3 id="tensorflow-models">TensorFlow models</h3>
<p>These are TensorFlow models that could be converted to .tflite and
then implemented in apps and things: * <a
href="https://github.com/tensorflow/models/tree/master/official">TensorFlow
models</a> - Official TensorFlow models. * <a
href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md">Tensorflow
detection model zoo</a> - Pre-trained on COCO, KITTI, AVA v2.1,
iNaturalist Species datasets.</p>
<h2 id="ideas-and-inspiration">Ideas and Inspiration</h2>
<ul>
<li><a href="https://github.com/ml-gde/e2e-tflite-tutorials">E2E TFLite
Tutorials</a> - 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.</li>
</ul>
<h2 id="ml-kit-examples">ML Kit examples</h2>
<p><a href="https://developers.google.com/ml-kit">ML Kit</a> is a mobile
SDK that brings Google’s ML expertise to mobile developers. * 2019-10-01
<a
href="https://codelabs.developers.google.com/codelabs/mlkit-android-translate/#0">ML
Kit Translate demo</a> - A tutorial with material design <a
href="https://github.com/googlecodelabs/mlkit-android/tree/master/translate">Android</a>
(Kotlin) sample - recognize, identify Language and translate text from
live camera with ML Kit for Firebase. * 2019-03-13 <a
href="https://youtu.be/ymyYUCrJnxU">Computer Vision with ML Kit -
Flutter In Focus</a>. * 2019-02-09 <a
href="https://medium.com/flutter-community/flutter-mlkit-8039ec66b6a">Flutter
+ MLKit: Business Card Mail Extractor</a> - A blog post with a <a
href="https://github.com/DaemonLoki/Business-Card-Mail-Extractor">Flutter</a>
sample code. * 2019-02-08 <a
href="https://speakerdeck.com/jinqian/from-tensorflow-to-ml-kit-power-your-android-application-with-machine-learning">From
TensorFlow to ML Kit: Power your Android application with machine
learning</a> - A talk with <a
href="https://github.com/xebia-france/magritte">Android</a> (Kotlin)
sample code. * 2018-08-07 <a
href="https://medium.com/over-engineering/building-a-custom-machine-learning-model-on-android-with-tensorflow-lite-26447e53abf2">Building
a Custom Machine Learning Model on Android with TensorFlow Lite</a>. *
2018-07-20 <a
href="https://flatteredwithflutter.com/ml-kit-and-face-detection-in-flutter/">ML
Kit and Face Detection in Flutter</a>. * 2018-07-27 <a
href="https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-landmark-detection-part-four-5e86b8deac3a">ML
Kit on Android 4: Landmark Detection</a>. * 2018-07-28 <a
href="https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-barcode-scanning-part-three-cc6f5921a108">ML
Kit on Android 3: Barcode Scanning</a>. * 2018-05-31 <a
href="https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-face-detection-part-two-de7e307c52e0">ML
Kit on Android 2: Face Detection</a>. * 2018-05-22 <a
href="https://medium.com/google-developer-experts/exploring-firebase-mlkit-on-android-introducing-mlkit-part-one-98fcfedbeee0">ML
Kit on Android 1: Intro</a>.</p>
<h2 id="plugins-and-sdks">Plugins and SDKs</h2>
<ul>
<li><a href="https://www.edgeimpulse.com/">Edge Impulse</a> - Created by
<a href="https://twitter.com/EdgeImpulse"><span class="citation"
data-cites="EdgeImpulse">@EdgeImpulse</span></a> to help you to train
TensorFlow Lite models for embedded devices in the cloud.</li>
<li><a href="https://github.com/google/mediapipe">MediaPipe</a> - A
cross platform (mobile, desktop and Edge TPUs) AI pipeline by Google AI.
(PM <a href="https://twitter.com/realmgyong">Ming Yong</a>) | <a
href="https://mediapipe.readthedocs.io/en/latest/examples.html">MediaPipe
examples</a>.</li>
<li><a href="https://coral.ai/">Coral Edge TPU</a> - Edge hardware by
Google. <a href="https://coral.ai/examples/">Coral Edge TPU
examples</a>.</li>
<li><a href="https://github.com/am15h/tflite_flutter_plugin/">TensorFlow
Lite Flutter Plugin</a> - Provides a dart API similar to the TensorFlow
Lite Java API for accessing TensorFlow Lite interpreter and performing
inference in flutter apps. <a
href="https://pub.dev/packages/tflite_flutter">tflite_flutter on
pub.dev</a>.</li>
</ul>
<h2 id="helpful-links">Helpful links</h2>
<ul>
<li><a href="https://github.com/lutzroeder/netron">Netron</a> - A tool
for visualizing models.</li>
<li><a href="http://ai-benchmark.com/tests.html">AI benchmark</a> - A
website for benchmarking computer vision models on smartphones.</li>
<li><a
href="https://www.tensorflow.org/lite/performance/measurement">Performance
measurement</a> - How to measure model performance on Android and
iOS.</li>
<li><a
href="https://material.io/collections/machine-learning/patterns-for-machine-learning-powered-features.html">Material
design guidelines for ML</a> - How to design machine learning powered
features. A good example: <a
href="https://github.com/firebase/mlkit-material-android">ML Kit
Showcase App</a>.</li>
<li><a href="https://pair.withgoogle.com/">The People + AI Guide
book</a> - Learn how to design human-centered AI products.</li>
<li><a
href="https://github.com/sayakpaul/Adventures-in-TensorFlow-Lite">Adventures
in TensorFlow Lite</a> - A repository showing non-trivial conversion
processes and general explorations in TensorFlow Lite.</li>
<li><a href="https://github.com/iglaweb/TFProfiler">TFProfiler</a> - An
Android-based app to profile TensorFlow Lite models and measure its
performance on smartphone.</li>
<li><a
href="https://www.tensorflow.org/lite/microcontrollers">TensorFlow Lite
for Microcontrollers</a></li>
<li><a
href="https://github.com/dailystudio/tensorflow-lite-examples-android">TensorFlow
Lite Examples - Android</a> - A repository refactors and rewrites all
the TensorFlow Lite Android examples which are included in the
TensorFlow official website.</li>
<li><a
href="https://github.com/SunitRoy2703/Tensorflow-lite-kotlin-samples">Tensorflow-lite-kotlin-samples</a>
- A collection of Tensorflow Lite Android example Apps in Kotlin, to
show different kinds of kotlin implementation of the <a
href="https://www.tensorflow.org/lite/examples">example apps</a></li>
</ul>
<h2 id="learning-resources">Learning resources</h2>
<p>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.</p>
<h3 id="blog-posts">Blog posts</h3>
<ul>
<li>2021-11-09 <a
href="https://blog.tensorflow.org/2021/11/on-device-training-in-tensorflow-lite.html">On-device
training in TensorFlow Lite</a></li>
<li>2021-09-27 <a
href="https://blog.tensorflow.org/2021/09/blog.tensorflow.org202109optical-character-recognition.html">Optical
character recognition with TensorFlow Lite: A new example app</a></li>
<li>2021-06-16 <a
href="https://blog.tensorflow.org/2021/11/on-device-training-in-tensorflow-lite.html">https://blog.tensorflow.org/2021/06/easier-object-detection-on-mobile-with-tf-lite.html</a></li>
<li>2020-12-29 <a
href="https://medium.com/analytics-vidhya/yolov3-to-tensorflow-lite-conversion-4602cec5c239">YOLOv3
to TensorFlow Lite Conversion</a> - By Nitin Tiwari.</li>
<li>2020-04-20 <a
href="https://blog.tensorflow.org/2020/04/whats-new-in-tensorflow-lite-from-devsummit-2020.html">What
is new in TensorFlow Lite</a> - By Khanh LeViet.</li>
<li>2020-04-17 <a
href="https://blog.tensorflow.org/2020/04/optimizing-style-transfer-to-run-on-mobile-with-tflite.html">Optimizing
style transfer to run on mobile with TFLite</a> - By Khanh LeViet and
Luiz Gustavo Martins.</li>
<li>2020-04-14 <a
href="https://blog.tensorflow.org/2020/04/how-tensorflow-lite-helps-you-from-prototype-to-product.html">How
TensorFlow Lite helps you from prototype to product</a> - By Khanh
LeViet.</li>
<li>2019-11-08 <a
href="https://blog.particle.io/2019/11/08/particle-machine-learning-101/">Getting
Started with ML on MCUs with TensorFlow</a> - By Brandon Satrom.</li>
<li>2019-08-05 <a
href="https://blog.tensorflow.org/2019/08/tensorflow-model-optimization-toolkit_5.html">TensorFlow
Model Optimization Toolkit — float16 quantization halves model size</a>
- By the TensorFlow team.</li>
<li>2018-07-13 <a
href="https://blog.tensorflow.org/2018/07/training-and-serving-realtime-mobile-object-detector-cloud-tpus.html">Training
and serving a real-time mobile object detector in 30 minutes with Cloud
TPUs</a> - By Sara Robinson, Aakanksha Chowdhery, and Jonathan
Huang.</li>
<li>2018-06-11 - <a
href="https://petewarden.com/2018/06/11/why-the-future-of-machine-learning-is-tiny/">Why
the Future of Machine Learning is Tiny</a> - By Pete Warden.</li>
<li>2018-03-30 - <a
href="https://blog.tensorflow.org/2018/03/using-tensorflow-lite-on-android.html">Using
TensorFlow Lite on Android</a>) - By Laurence Moroney.</li>
</ul>
<h3 id="books">Books</h3>
<ul>
<li>2021-12-01 <a
href="https://learning.oreilly.com/library/view/ai-and-machine/9781098101732/">AI
and Machine Learning On-Device Development</a> (early access) - By
Laurence Moroney (<a href="https://twitter.com/lmoroney"><span
class="citation" data-cites="lmoroney">@lmoroney</span></a>).</li>
<li>2020-10-01 <a
href="https://learning.oreilly.com/library/view/ai-and-machine/9781492078180/">AI
and Machine Learning for Coders</a> - By Laurence Moroney (<a
href="https://twitter.com/lmoroney"><span class="citation"
data-cites="lmoroney">@lmoroney</span></a>).</li>
<li>2020-04-06 <a
href="https://www.packtpub.com/product/mobile-deep-learning-with-tensorflow-lite-ml-kit-and-flutter/9781789611212">Mobile
Deep Learning with TensorFlow Lite, ML Kit and Flutter</a>: Build
scalable real-world projects to implement end-to-end neural networks on
Android and iOS (<a
href="https://github.com/PacktPublishing/Mobile-Deep-Learning-Projects">GitHub</a>)
- By Anubhav Singh (<a href="https://github.com/xprilion"><span
class="citation" data-cites="xprilion">@xprilion</span></a>) and Rimjhim
Bhadani (<a href="https://github.com/Rimjhim28"><span class="citation"
data-cites="Rimjhim28">@Rimjhim28</span></a>).</li>
<li>2020-03-01 Raspberry Pi for Computer Vision (<a
href="https://www.pyimagesearch.com/raspberry-pi-for-computer-vision">Complete
Bundle</a> | <a
href="https://www.pyimagesearch.com/2019/04/05/table-of-contents-raspberry-pi-for-computer-vision/">TOC</a>)
- By the PyImageSearch Team: Adrian Rosebrock (<a
href="https://twitter.com/PyImageSearch"><span class="citation"
data-cites="PyImageSearch">@PyImageSearch</span></a>), David Hoffman,
Asbhishek Thanki, Sayak Paul (<a
href="https://twitter.com/RisingSayak"><span class="citation"
data-cites="RisingSayak">@RisingSayak</span></a>), and David
Mcduffee.</li>
<li>2019-12-01 <a
href="http://shop.oreilly.com/product/0636920254508.do">TinyML</a> - By
Pete Warden (<a href="https://twitter.com/petewarden"><span
class="citation" data-cites="petewarden">@petewarden</span></a>) and
Daniel Situnayake (<a href="https://twitter.com/dansitu"><span
class="citation" data-cites="dansitu">@dansitu</span></a>).</li>
<li>2019-10-01 <a href="https://www.practicaldeeplearning.ai/">Practical
Deep Learning for Cloud, Mobile, and Edge</a> - By Anirudh Koul (<a
href="https://twitter.com/AnirudhKoul"><span class="citation"
data-cites="AnirudhKoul">@AnirudhKoul</span></a>), Siddha Ganju (<a
href="https://twitter.com/SiddhaGanju"><span class="citation"
data-cites="SiddhaGanju">@SiddhaGanju</span></a>), and Meher Kasam (<a
href="https://twitter.com/MeherKasam"><span class="citation"
data-cites="MeherKasam">@MeherKasam</span></a>).</li>
</ul>
<h3 id="videos">Videos</h3>
<ul>
<li>2021-10-06 <a href="https://youtu.be/sZayUoWW6nE">Contributing to
TensorFlow Lite with Sunit Roy</a> (Hacktoberfest 2021)</li>
<li>2020-07-25 <a href="https://youtu.be/m_bEh8YifnQ">Android ML by Hoi
Lam</a> (GDG Kolkata meetup).</li>
<li>2020-04-01 <a href="https://youtu.be/ALxWJoh_BHw">Easy on-device ML
from prototype to production</a> (TF Dev Summit 2020).</li>
<li>2020-03-11 <a href="https://youtu.be/27Zx-4GOQA8">TensorFlow Lite:
ML for mobile and IoT devices</a> (TF Dev Summit 2020).</li>
<li>2019-10-31 <a href="https://youtu.be/zjDGAiLqGk8">Keynote -
TensorFlow Lite: ML for mobile and IoT devices</a>.</li>
<li>2019-10-31 <a href="https://youtu.be/0SpZy7iouFU">TensorFlow Lite:
Solution for running ML on-device</a>.</li>
<li>2019-10-31 <a href="https://youtu.be/3JWRVx1OKQQ">TensorFlow model
optimization: Quantization and pruning</a>.</li>
<li>2019-10-29 <a href="https://youtu.be/gHN0jDbJz8E">Inside TensorFlow:
TensorFlow Lite</a>.</li>
<li>2018-04-18 <a href="https://youtu.be/JnhW5tQ_7Vo">TensorFlow Lite
for Android (Coding TensorFlow)</a>.</li>
</ul>
<h3 id="podcasts">Podcasts</h3>
<ul>
<li>2020-08-08 <a
href="https://anchor.fm/talkingwithapples/episodes/Talking-Machine-Learning-with-Hoi-Lam-eiaj7v">Talking
Machine Learning with Hoi Lam</a>.</li>
</ul>
<h3 id="moocs">MOOCs</h3>
<ul>
<li><a
href="https://www.udacity.com/course/intro-to-tensorflow-lite--ud190">Introduction
to TensorFlow Lite</a> - Udacity course by Daniel Situnayake (<span
class="citation" data-cites="dansitu">@dansitu</span>), Paige Bailey (<a
href="https://twitter.com/DynamicWebPaige"><span class="citation"
data-cites="DynamicWebPaige">@DynamicWebPaige</span></a>), and Juan
Delgado.</li>
<li><a
href="https://www.coursera.org/learn/device-based-models-tensorflow">Device-based
Models with TensorFlow Lite</a> - Coursera course by Laurence Moroney
(<a href="https://twitter.com/lmoroney"><span class="citation"
data-cites="lmoroney">@lmoroney</span></a>).</li>
<li><a
href="https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning">The
Future of ML is Tiny and Bright</a> - A series of edX courses created by
Harvard in collaboration with Google. Instructors - Vijay Janapa Reddi,
Laurence Moroney, and Pete Warden.</li>
</ul>
<p><a
href="https://github.com/margaretmz/awesome-tensorflow-lite">tensorflowlite.md
Github</a></p>