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An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources.
| Date | Stars |
|---|---|
| 2026-07-30 | 1382 |
| 2026-08-10 | 1387 |
| 2026-08-20 | 1389 |
| 2026-08-21 | 1389 |
| 2026-08-25 | 1388 |
| 2026-08-26 | 1390 |
| 2026-08-28 | 1392 |
| 2026-09-08 | 1393 |
| 2026-09-09 | 1392 |
| 2026-09-10 | 1392 |
| 2026-09-20 | 1391 |
| 2026-09-21 | 1392 |
| 2026-09-23 | 1391 |
| 2026-09-28 | 1390 |
| 2026-10-01 | 1391 |
| 2026-10-03 | 1391 |
| 2026-10-09 | 1392 |
| 2026-10-10 | 1393 |
| 2026-10-12 | 1394 |
| 2026-10-22 | 1394 |
| 2026-10-26 | 1395 |
| 2026-10-28 | 1396 |
| 2026-11-04 | 1395 |
Today
-1 stars today
This week
-1 stars this week
This month
+4 stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center">
<img src="images/awesome-tflite.png" alt="awesome tflite" width="500">
</p>
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# 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).
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## 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 inferencExcerpt of 41,788 characters
Read on GitHub
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Sayak Paul · @huggingface · India
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Tulasi Ram · India
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@SpeechifyInc
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Aritra Roy Gosthipaty · @huggingface · India
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Zilin Zhu · Z.ai · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3041b862b135c177, topic:awesome, topic:awesome-list, desc:awesome list
matched fp:3041b862b135c177, topic:deep-learning, topic:tensorflow
matched fp:3041b862b135c177, topic:computer-vision, readme:computer vision, readme:image classification