Getting Started

Importing NatML

To begin, import NatML to your Unity project by adding the following lines to your Unity project's Packages > manifest.json file:

{
  "scopedRegistries": [
   {
    "name": "NatML",
    "url": "https://registry.npmjs.com",
    "scopes": ["ai.natml"]
   }
  ],
  "dependencies": {
    "ai.natml.natml": "1.1.8",
    ...
  }
}

Once NatML has been imported, you can run ML models right in the Editor. For this example, we will classify an image using the popular MobileNet classifier architecture:

@natsuite/mobilenet-v2 - NatML

Importing the MobileNet Predictor

First, we'll import the MobileNet predictor into our project by following the import instructions:

{
  ...,
  "dependencies": {
    "ai.natml.natml": "1.1.8",
    "ai.natml.vision.mobilenet-v2": "1.0.3",
    ...
  }
}

Specifying your NatML Access Key

We will fetch our models from NatML Hub, . To do so, we need to retrieve our access key:

https://hub.natml.ai/profile

Once you have an access key, you can add it to your Unity project in Project Settings > NatML:

Importing a Sample Image

Next, import the image below (download it into your Unity project):

Make sure that in the advanced settings, you enable the Read/Write Enabled setting:

Making a Prediction

Now, we can write a small script to classify the image:

using UnityEngine;
using NatML.Vision;

public class Classifier : MonoBehaviour {
    
    [Header(@"Prediction")]
    public Texture2D image;
    
    async void Start () {
        // Create the MobileNet predictor
        using var predictor = await MobileNetv2Predictor.Create();
        // Classify the image feature
        var (label, confidence) = predictor.Predict(image);
        // Log the result
        Debug.Log($"Image contains {label} with confidence {confidence}");     
    }
}

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!