Unity
  • NatML for Unity
  • Preliminaries
    • Getting Started
    • Requirements
  • Workflows
    • Core Concepts
    • Fetching Models
    • Using Predictors
  • Authoring
    • Creating Predictors
    • Distributing Predictors
  • API Reference
    • IMLPredictor
    • MLModel
      • MLEdgeModel
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      • MLArrayFeature
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      • MLXRCpuDepthFeature
    • MLFeatureType
      • MLArrayType
      • MLAudioType
      • MLImageType
      • MLVideoType
      • MLStringType
    • MLPredictorExtensions
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    • Augmented Reality
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  • Fetching from Hub
  • Using Model Files

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  1. Workflows

Fetching Models

Where it All Begins

PreviousCore ConceptsNextUsing Predictors

Last updated 2 years ago

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The very first step in using ML in your app is fetching a model. NatML supports fetching models from different sources:

Fetching from Hub

is a platform for managing and deploying ML models.

NatML Hub provides a predictor catalog from which models can be fetched:

// Create an edge model
var model = await MLEdgeModel.Create("@natsuite/yolox");

Predictors fetched from NatML are cached on-device, so your users only ever have to download the model once.

Using Model Files

You will need a NatML access key to fetch models from Hub. for how to get your access key.

When you upload your model to , we will automatically convert your model to CoreML, ONNX, and TensorFlow Lite, making your model cross-platform.

NatML supports using (.mlmodel), (.onnx), and (.tflite) models. Simply drag and drop the model file into your Unity project. The model file is imported as an MLModelData instance.

There are restrictions on what ML model files can be used on which platform. for more info.

NatML Hub
CoreML
ONNX
TensorFlow Lite
NatML Hub
See this guide
See the docs
The NatML predictor catalog.
Dropping a CoreML model into Unity.