Getting Started

To begin, import the API to your Unity project. Once NatML has been imported, you can run ML models right in the Editor. For this example, we will classify this image using the popular MobileNet classifier architecture:
A Cat
First, import the image (download the image above). Make sure that in the advanced settings, you enable the Read/Write Enabled setting:
Import the cat image.
Now, we can write a small script to classify the image:
Classifier.cs
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using UnityEngine;
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using NatSuite.ML;
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using NatSuite.ML.Features;
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using NatSuite.ML.Vision;
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​
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public class Classifier : MonoBehaviour {
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    [Header(@"NatML Hub")]
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    public string accessKey;
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    [Header(@"Prediction")]
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    public Texture2D image;
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    async void Start () {
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        // Fetch the model data from Hub
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        Debug.Log("Fetching model data from Hub");
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        var modelData = await MLModelData.FromHub("@natsuite/mobilenet-v2", accessKey);
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        // Deserialize the model
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        var model = modelData.Deserialize();
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        // Create the classification predictor
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        var predictor = new MobileNetv2Predictor(model, modelData.labels);
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        // Create an image feature and apply normalization
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        var input = new MLImageFeature(image);
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        (input.mean, input.std) = modelData.normalization;
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        input.aspectMode = modelData.aspectMode;
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        // Classify the image
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        var (label, confidence) = predictor.Predict(input);
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        // Log the result
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        Debug.Log(quot;Image contains {label} with confidence {confidence}");
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        // Dispose the model
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        model.Dispose();        
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    }
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}
Copied!
You can get your accessKey on NatML Hub. This is how Hub is able to identify you and provide you with models that you have access to.
Now, let us add our script to the scene and assign the inspector variables:
Now, we run the script to confirm that our model predicted the image correctly!
It's a cat!

NatML Unity SDK

Next - Preliminaries

Requirements

Last modified 1d ago

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