> For the complete documentation index, see [llms.txt](https://docs.natml.ai/unity/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.natml.ai/unity/prelims/getting-started.md).
# Getting Started
Quick Primer
## Importing NatML
To begin, [import NatML to your Unity project](https://github.com/natmlx/NatML#installing-natml) 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](https://hub.natsuite.io/predictor/@natsuite/mobilenet-v2) architecture:
{% embed url="" %}
Get the MobileNet v2 predictor from NatML Hub.
{% endembed %}
## Importing the MobileNet Predictor
First, we'll import the MobileNet predictor into our project by following [the import instructions](https://github.com/natml-hub/MobileNet-v2/tree/main/Packages/ai.natml.vision.mobilenet-v2):
{
...,
"dependencies": {
"ai.natml.natml": "1.1.8",
"ai.natml.vision.mobilenet-v2": "1.0.3",
...
}
}
{% hint style="info" %}
The recommended way to make predictions with ML models is using [Predictors](/unity/workflows/concepts.md#predictors). These are lightweight classes that 'know how' to format the model's input and output features.
{% endhint %}
## Specifying your NatML Access Key
We will fetch our models from [NatML Hub](https://hub.natml.ai/profile), . To do so, we need to retrieve our access key:
{% embed url="" %}
Get your access key from NatML Hub
{% endembed %}
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:
{% code title="Classifier.cs" %}
```csharp
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}");
}
}
```
{% endcode %}
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!
