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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The challenge projects for Inferencing machine learning models on iOS
| Date | Stars |
|---|---|
| 2026-07-24 | 1292 |
| 2026-07-25 | 1292 |
| 2026-07-28 | 1292 |
| 2026-07-30 | 1292 |
| 2026-07-31 | 1293 |
| 2026-08-06 | 1290 |
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<p align="center"> <img src="Resource/awesome-ml-demos-with-ios-logo.png" width="187" height="174"/> </p> [](https://github.com/motlabs/awesome-ml-demos-with-ios)  [](http://makeapullrequest.com) [](http://makeapullrequest.com) > This repo was moved from [@motlabs](https://github.com/motlabs) group. Thanks for [@jwkanggist](https://github.com/jwkanggist) who is a leader of motlabs community. # Awesome Machine Learning DEMOs with iOS We tackle the challenge of using machine learning models on iOS via Core ML and ML Kit (TensorFlow Lite). [한국어 README](https://github.com/motlabs/iOS-Proejcts-with-ML-Models/blob/master/README_kr.md) ## Contents - [Machine Learning Framework for iOS](#machine-learning-framework-for-ios) - [Flow of Model When Using Core ML](#Flow-of-Model-When-Using-Core-ML) - [Flow of Model When Using Create ML](#Flow-of-Model-When-Using-Create-ML) - [Baseline Projects](#Baseline-Projects) - [Image Classification](#Image-Classification) - [Object Detection & Recognition](#Object-Detection--Recognition) - [Image Estimation](#Image-Estimation) - [Semantic Segmentation](#Semantic-Segmentation) - [Application Projects](#Application-Projects) - [Annotation Tool](#Annotation-Tool) - [Create ML Projects](#Create-ML-Projects) - [Performance](#Performance) - [📏Measure module](#measure-module) - [Implements](#Implements) - [See also](#See-also) ## Machine Learning Framework for iOS - [Core ML](https://developer.apple.com/documentation/coreml) - [TensorFlow Lite](https://www.tensorflow.org/lite) - [Pytorch Mobile](https://pytorch.org/mobile/home/) - [fritz](https://www.fritz.ai/) - etc. ~~[Tensorflow Mobile](https://www.tensorflow.org/mobile/)~~`DEPRECATED`) ### Flow of Model When Using Core ML [](https://docs.google.com/presentation/d/1wA_PAjllpLLcFPuZcERYbQlPe1Ipb-bzIZinZg3zXkg/edit?usp=sharing) The overall flow is very similar for most ML frameworks. Each framework has its own compatible model format. We need to take the model created in TensorFlow and **convert it into the appropriate format, for each mobile ML framework**. Once the compatible model is prepared, you can run the inference using the ML framework. Note that you must perform **pre/postprocessing** manually. > If you want more explanation, check [this slide(Korean)](https://docs.google.com/presentation/d/1wA_PAjllpLLcFPuZcERYbQlPe1Ipb-bzIZinZg3zXkg/edit?usp=sharing). ### Flow of Model When Using Create ML  ## Baseline Projects #### DONE - Using built-in model with Core ML - Using built-in on-device model with ML Kit - Using custom model for Vision with Core ML and ML Kit - Object Detection with Core ML #### TODO - Object Detection with ML Kit - Using built-in cloud model on ML Kit - Landmark recognition - Using custom model for NLP with Core ML and ML Kit - Using custom model for Audio with Core ML and ML Kit - Audio recognition - Speech recognition - TTS ### Image Classification | Name | DEMO | Note | | ---- | ---- | ---- | | [ImageClassification-CoreML](https://github.com/tucan9389/ImageClassification-CoreML) | <p align="center"><img src="Resource/MobileNet-CoreML-DEMO.gif" width="200"/></p> | - | | [MobileNet-MLKit](https://github.com/tucan9389/MobileNet-MLKit) | <p align="center"><img src="Resource/MobileNet-MLKit-DEMO.gif" width="200"/></p> | - | ### Object Detection & Recognition | Name | DEMO | Note |
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matched fp:b58c5eafeea755ef, topic:inference
matched fp:b58c5eafeea755ef, topic:tensorflow
matched fp:b58c5eafeea755ef, topic:awesome