# AI/ML

{% hint style="info" %}
If using an downloaded image, check first that these packages are not already installed.
{% endhint %}

## Inference examples

The i.MX 8M Plus SOC in the NavQPlus contains a Neural Processing Unit (NPU) that can accelerate inference on the edge. There are some inference examples built into the NavQPlus image that will do image classification with the COCO dataset.

There is a TensorFlow Lite example in the `/usr/bin/tensorflow-lite-2.6.0/examples/` folder. This example runs a mobilenet\_v1 quantized image classification model. To run it, try the command below:

```
USE_GPU_INFERENCE=0 ./label_image --external_delegate_path=/lib/libvx_delegate.so -m mobilenet_v1_1.0_224_quant.tflite -i grace_hopper.bmp -l labels.txt
```

`USE_GPU_INFERENCE=0` specifies that we want to run inference on the NPU. Removing this environment variable will run inference on the GPU.

To run inference on the GPU/NPU, you will need to use the libVX delegate, at the path `/lib/libvx_delegate.so`.

There is also a python script named label\_image.py that contains example code for inference with TFLite.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://nxp.gitbook.io/navqplus/user-contributed-content/ai.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.