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Core ML FormatGithub
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Overview

  • Core ML Tools Overview
    • Contributing
    • Contribution Guidelines
    • GitHub
  • New Features
    • Release Notes
    • Migration Workflow
  • Core ML Tools FAQs

Essentials

  • Installing Core ML Tools
  • Getting Started
  • Unified Conversion API
  • Core ML Model Format

Unified Conversion

  • Converting Source Models
  • ML Programs
    • Comparing ML Programs and Neural Networks
    • Typed Execution
    • Typed Execution Workflow Example
  • Converting from TensorFlow
    • TensorFlow 1 Workflow
    • Converting a TensorFlow 1 Image Classifier
    • Converting a TensorFlow 1 DeepSpeech Model
    • TensorFlow 2 Workflow
    • Converting TensorFlow 2 BERT Transformer Models
  • Converting from PyTorch
    • Model Tracing
    • Model Scripting
    • Converting a Natural Language Processing Model
    • Converting a torchvision Model from PyTorch
    • Converting a PyTorch Segmentation Model
  • Conversion Options
    • Model Input and Output Types
    • Image Input and Output
    • Classifiers
    • Flexible Input Shapes
    • Composite Operators
    • Custom Operators
    • Graph Passes
  • Model Intermediate Language

Model optimization

  • Compressing ML Program Weights
  • Compressing Neural Network Weights

Other Converters

  • LibSVM
  • Scikit-learn
  • XGBoost

MLModel

  • MLModel Overview
  • Xcode Model Preview Types
  • MLModel Utilities
  • Model Prediction
  • Updatable Models Overview
    • Neural Network Classifier
    • Pipeline Classifier
    • Nearest Neighbor Classifier

Updatable Models Overview

Suggest Edits

Core ML allows you to personalize models using the MLUpdateTask API. Three types of models are updatable: neural networks, pipeline models, and nearest neighbors. For examples, see the following:

  • Updatable neural network classifier
  • Updatable pipeline model
  • Updatable nearest neighbors classifier

Updated over 5 years ago


What’s Next

  • Neural Network Classifier
  • Pipeline Classifier
  • Nearest Neighbor Classifier
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