Unet-Segmentation Example

This document explains how to use the QAI AppBuilder Python API to perform inference with the Unet-Segmentation image segmentation model using the Qualcomm® Hexagon™ Processor (NPU).

Supported devices

Device	SoC
Fogwise® AIRbox Q900	QCS9075

Install QAI AppBuilder

tip

1. Install QAI AppBuilder by following the QAI AppBuilder installation guide.

2. Configure ADSP environment variables as described in Create ADSP environment variables.

Run the sample

Install dependencies

Install sample dependencies in the activated virtual environment:

Device

pip3 install requests tqdm qai-hub py3-wget Pillow torch torchvision opencv-python-headless

Run the script

• Enter the upstream samples directory

  Device

  cd qai-appbuilder/samples

• Prepare input data (use the sample input if provided, or pass script arguments)

Input image

• Run inference

  Device

  python3 ComputerVision/Semantic_Segmentation/unet_segmentation/unet_segmentation.py --chipset 9075

• Example result

Output image

tip

The first run downloads the model via Qualcomm® AI Hub (network-dependent). You can also use the launcher from samples:

python3 run_inference.py --list
python3 run_inference.py --model unet_segmentation --args "--chipset 9075"