Examples
The following are code example snippets and full examples of using Core ML Tools to convert models.
| Topic | Example |
|---|---|
| For a quick start | Getting Started: Demonstrates how to convert an image classifier model trained using the TensorFlow Keras API to the Core ML format. |
| ML program with typed execution | Typed Execution Workflow Example: Demonstrates a workflow for checking accuracy using ML Programs with Typed Execution. |
| TensorFlow 2 | Load and Convert a Model (in Unified Conversion API) TensorFlow 2 Workflow Convert a Pre-trained Model Convert a User-defined Model Full examples: Getting Started: Demonstrates how to convert an image classifier model trained using the TensorFlow Keras API to the Core ML format. Converting TensorFlow 2 BERT Transformer Models: Converts an object of the tf.keras.Model class and a SavedModel in the TensorFlow 2 format. |
| TensorFlow 1 | Convert From TensorFlow 1 (in Unified Conversion API) Export as Frozen Graph and Convert Convert a Pre-trained Model Full examples: Converting a TensorFlow 1 Image Classifier: Demonstrates the importance of setting the image preprocessing parameters correctly during conversion to get the right results. Converting a TensorFlow 1 DeepSpeech Model: Demonstrates automatic handling of flexible shapes using automatic speech recognition. |
| PyTorch | Convert from PyTorch (in Unified Conversion API) Converting from PyTorch Model Tracing Model Scripting Full examples: Converting a Natural Language Processing Model: Combines tracing and scripting to convert a PyTorch natural language processing model. Converting a torchvision Model from PyTorch: Traces a torchvision MobileNetV2 model, adds preprocessing for image input, and then converts it to Core ML. Converting a PyTorch Segmentation Model: Converts a PyTorch segmentation model that takes an image and outputs a class prediction for each pixel of the image. |
| Model Intermediate Language (MIL) | Model Intermediate Language: Construct a MIL program using the Python builder. |
| Conversion Options | Image Inputs: Convert a Model with a MultiArray Convert a Model with an ImageType Add Image Preprocessing Options Classifiers: Produce a Classifier Model Flexible Input Shapes: Select from Predetermined Shapes Set the Range for Each Dimension Update a Core ML Model to Use Flexible Input shapes Composite Operators: Defining a composite operation by decomposing it into MIL operations. Full examples: Custom Operators: Augment Core ML with your own operators and implement them in Swift. |
| Optimization | Training-Time Compression Examples: Use magnitude pruning, linear quantization, or palettization while training your model, or start from a pre-trained model and fine-tune it with training data. Compressing Neural Network Weights: Reduce the size of a neural network by reducing the number of bits that represent a number. |
| Other Converters | Multi-backend Keras ONNX Caffe |
| Trees and Linear Models | LibSVM Scikit-learn XGBoost |
| MLModel | MLModel Overview: Load and save the MLModel Use the MLModel for Prediction Work with the spec Object Update the Metadata and Input/output Descriptions Model Prediction: Make Predictions Multi-array Prediction Image Prediction Image Prediction for a Multi-array Model Xcode Model Preview Types: Segmentation Example BERT QA Example Body Pose Example MLModel Utilities: Rename a Feature Convert All Double Multi-array Feature Descriptions to Float Evaluate Classifier, Regressor, and Transformer models |
| Updatable Models | Nearest Neighbor Classifier: Create an updatable empty k-nearest neighbor. Neural Network Classifier: Create a simple convolutional model with Keras, convert the model to Core ML, and make the model updatable. Pipeline Classifier: Use a pipeline composed of a drawing-embedding model and a nearest neighbor classifier to create a model for training a sketch classifier. |
If you have a code example you'd like to submit, see Contributing.