Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/bhoke/awesome-embedded-ml

This is a list of interesting papers and projects about Embedded machine learning projects.
https://github.com/bhoke/awesome-embedded-ml
List: awesome-embedded-ml
Last synced: 1 day ago
JSON representation
This is a list of interesting papers and projects about Embedded machine learning projects.
Host: GitHub
URL: https://github.com/bhoke/awesome-embedded-ml
Owner: bhoke
License: apache-2.0
Created: 2022-07-17T13:02:11.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-02-03T17:28:35.000Z (almost 2 years ago)
Last Synced: 2024-10-28T23:01:54.334Z (about 2 months ago)
Size: 543 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 1
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project

ultimate-awesome - awesome-embedded-ml - This is a list of interesting papers and projects about Embedded machine learning projects. (Other Lists / PowerShell Lists)
README

        
# Awesome Embedded Machine Learning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)

This is an awesome list of machine learning on embedded devices including models with sample apps, helpful tools and learning resources

* Showcase what the community has built

* 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

- [Model zoo](#model-zoo)

  - [Computer vision](#computer-vision)

    - [Classification](#classification)

    - [Detection](#detection)

    - [Other](#other)

  - [Text](#text)

  - [Speech](#speech)

- [Ideas and Inspiration](#ideas-and-inspiration)

- [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)

### Computer vision

#### Classification

| 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))| [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     |

#### Detection

| Model | App/Reference | Source |

| -|-|-|

| 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)) |  [Overview](https://www.tensorflow.org/lite/models/object_detection/overview#starter_model)                                                                                                                     | tensorflow.org     |

#### 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) |                                                                                                                     | 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

### 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) | Community |

| Speech Synthesis   | Tacotron-2, FastSpeech2, MB-Melgan |  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  |

## 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.

## 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.

## 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/).

## Helpful links

* [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.

* [Model Metadata](https://www.tensorflow.org/lite/convert/metadata) - Provides a standard for model descriptions.

* [Netron](https://github.com/lutzroeder/netron) - A tool for visualizing models.

* [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.

* [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers)

### 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.

### Books

* 2021-12-01 [AI and Machine Learning On-Device Development](https://www.oreilly.com/library/view/ai-and-machine/9781098101732/) - By Laurence Moroney

* 2020-10-01 [AI at the Edge](https://www.oreilly.com/library/view/ai-at-the/9781098120191/) - By Daniel Situnayake and Jenny Plunket.

* 2019-12-01 [TinyML](http://shop.oreilly.com/product/0636920254508.do) - By Pete Warden and Daniel Situnayake.

* 2019-10-01 [Practical Deep Learning for Cloud, Mobile, and Edge](https://www.practicaldeeplearning.ai/) - By Anirudh Koul, Siddha Ganju, and Meher Kasam.

### Videos

* [Contributing to TensorFlow Lite with Sunit Roy](https://youtu.be/sZayUoWW6nE) (Hacktoberfest 2021)

* [Easy on-device ML from prototype to production](https://youtu.be/ALxWJoh_BHw) (TF Dev Summit 2020).

* [TensorFlow Lite: ML for mobile and IoT devices](https://youtu.be/27Zx-4GOQA8) (TF Dev Summit 2020).

* [Keynote - TensorFlow Lite: ML for mobile and IoT devices](https://youtu.be/zjDGAiLqGk8).

* [TensorFlow Lite: Solution for running ML on-device](https://youtu.be/0SpZy7iouFU).

* [TensorFlow model optimization: Quantization and pruning](https://youtu.be/3JWRVx1OKQQ).

* [Inside TensorFlow: TensorFlow Lite](https://youtu.be/gHN0jDbJz8E).

### MOOCs

* [Introduction to TensorFlow Lite](https://www.udacity.com/course/intro-to-tensorflow-lite--ud190) - Udacity course by Daniel Situnayake (@dansitu), Paige Bailey, and Juan Delgado.

* [Device-based Models with TensorFlow Lite](https://www.coursera.org/learn/device-based-models-tensorflow) - Coursera course by Laurence Moroney.

* [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.