Deploying to Core ML

With the Core ML framework, you can use a machine learning model to classify input data. Exporting this model in Core ML format can be performed using the export_coreml function.

model.export_coreml('MyCatDogClassifier.mlmodel')

When you open the model in Xcode, it looks like the following:

Through a simple drag and drop process, you can incorporate the model into Xcode. The following Swift code is needed to consume the model in an iOS app.

let model = try VNCoreMLModel(for: MyCustomImageClassifier().model)

let request = VNCoreMLRequest(model: model, completionHandler: { [weak self] request, error in
    self?.processClassifications(for: request, error: error)
})
request.imageCropAndScaleOption = .centerCrop
return request

Refer to the Core ML sample application for more details on using image classifiers in Core ML and Vision frameworks for iOS and macOS.