한국어 README

Core ML

is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine…

TensorFlow 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…

Pytorch Mobile

fritz

ImageClassification-CoreML

MobileNet-MLKit

ObjectDetection-CoreML

TextDetection-CoreML

TextRecognition-MLKit

FaceDetection-MLKit

PoseEstimation-CoreML

PoseEstimation-TFLiteSwift

PoseEstimation-MLKit

FingertipEstimation-CoreML

DepthPrediction-CoreML

SemanticSegmentation-CoreML

dont-be-turtle-ios

WordRecognition-CoreML-MLKit

Detect character, find a word what I point and then recognize the word using Core ML and ML Kit.

KeypointAnnotation

Annotation tool for own custom estimation dataset

SimpleClassification-CreateML-CoreML

A Simple Classification Using Create ML and Core ML

Core ML

is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine…

Machine Learning - Apple Developer

ML Kit - Firebase

is a mobile SDK that brings Google's ML expertise to mobile developers.

Apple's Core ML 2 vs. Google's ML Kit: What's the difference?

iOS에서 머신러닝 슬라이드 자료

MoT Labs Blog

WWDC2020 10152 Session - Use model deployment and security with Core ML

WWDC2020 10153 Session - Get models on device using Core ML Converters

WWDC2020 10673 Session - Explore Computer Vision APIs

WWDC2020 10099 Session - Explore the Action & Vision app

WWDC2020 10653 Session - Detect Body and Hand Pose with Vision

TECH-TALKS 206 Session - QR Code Recognition on iOS 11

WWDC2020 10657 Session - Make apps smarter with Natural Language

WWDC2019 256 Session - Advances in Speech Recognition

WWDC2019 704 Session - Core ML 3 Framework

WWDC2019 228 Session - Creating Great Apps Using Core ML and ARKit

WWDC2019 232 Session - Advances in Natural Language Framework

WWDC2019 222 Session - Understanding Images in Vision Framework

WWDC2019 234 Session - Text Recognition in Vision Framework

WWDC2018 708 Session - What’s New in Core ML, Part 1

WWDC2018 716 Session - Object Tracking in Vision

WWDC2018 717 Session - Vision with Core ML

WWDC2018 709 Session - What’s New in Core ML, Part 2

WWDC2018 713 Session - Introducing Natural Language Framework

WWDC2017 710 Session - Core ML in depth

WWDC2017 208 Session - Natural Language Processing and your Apps

WWDC2017 510 Session - Advances in Core Image: Filters, Metal, Vision, and More

WWDC2017 506 Session - Vision Framework: Building on Core ML

WWDC2017 703 Session - Introducing Core ML

WWDC2020 10642 Session - Build Image and Video Style Transfer models in Create ML

WWDC2020 10156 Session - Control training in Create ML with Swift

WWDC2020 10043 Session - Build an Action Classifier with Create ML

WWDC2019 424 Session - Training Object Detection Models in Create ML

WWDC2019 426 Session - Building Activity Classification Models in Create ML

WWDC2019 420 Session - Drawing Classification and One-Shot Object Detection in Turi Create

WWDC2019 425 Session - Training Sound Classification Models in Create ML

WWDC2019 428 Session - Training Text Classifiers in Create ML

WWDC2019 427 Session - Training Recommendation Models in Create ML

WWDC2019 430 Session - Introducing the Create ML App

WWDC2018 712 Session - A Guide to Turi Create

WWDC2018 703 Session - Introducing Create ML

WWDC2020 10677 Session - Build customized ML models with the Metal Performance Shaders Graph

WWDC2019 803 Session - Designing Great ML Experiences

WWDC2019 614 Session - Metal for Machine Learning

WWDC2019 209 Session - What's New in Machine Learning

WWDC2018 609 Session - Metal for Accelerating Machine Learning

WWDC2016 715 Session - Neural Networks and Accelerate

WWDC2016 605 Session - What's New in Metal, Part 2

WWDC2020 10632 Session - Optimize Metal Performance for Apple Silicon Macs

WWDC2020 10603 Session - Optimize Metal apps and games with GPU counters

TECH-TALKS 606 Session - Metal 2 on A11 - Imageblock Sample Coverage Control

TECH-TALKS 603 Session - Metal 2 on A11 - Imageblocks

TECH-TALKS 602 Session - Metal 2 on A11 - Overview

TECH-TALKS 605 Session - Metal 2 on A11 - Raster Order Groups

TECH-TALKS 604 Session - Metal 2 on A11 - Tile Shading

TECH-TALKS 608 Session - Metal Enhancements for A13 Bionic

WWDC2020 10631 Session - Bring your Metal app to Apple Silicon Macs

WWDC2020 10197 Session - Broaden your reach with Siri Event Suggestions

WWDC2020 10615 Session - Build GPU binaries with Metal

WWDC2020 10021 Session - Build Metal-based Core Image kernels with Xcode

WWDC2020 10616 Session - Debug GPU-side errors in Metal

WWDC2020 10012 Session - Discover ray tracing with Metal

WWDC2020 10013 Session - Get to know Metal function pointers

WWDC2020 10605 Session - Gain insights into your Metal app with Xcode 12

WWDC2020 10602 Session - Harness Apple GPUs with Metal

TECH-TALKS 609 Session - Advanced Scene Understanding in AR

TECH-TALKS 601 Session - Face Tracking with ARKit

WWDC2020 10611 Session - Explore ARKit 4

WWDC2020 10604 Session - Shop online with AR Quick Look

WWDC2020 10601 Session - The artist’s AR toolkit

WWDC2020 10613 Session - What's new in USD

Keras examples:

Pytorch examples:

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

TFLite examples:

Pytorch Mobile iOS example:

FritzLabs examples:

TensorFlow & TFLite models:

Set "Model format = TFLite" to find TensorFlow Lite models.

Pytorch models:

CoreML official models: