> ## Documentation Index
> Fetch the complete documentation index at: https://dragonwingdocs.qualcomm.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Model porting best practices

> Export and quantize a custom YOLOv8 model using the Qualcomm AI Runtime (QAIRT) SDK and run it on a Qualcomm evaluation kit.

## Export a custom YOLOv8 model using the QAIRT SDK

### Prerequisites

Install the Qualcomm AI Runtime SDK on a host computer with `Python >= 3.10` and `PyTorch >=1.8`.

For more details, follow [Install Qualcomm AI Runtime SDK](../topic/qairt-install).

Run the following commands on the host computer.

1. Activate your virtual environment.

   ```shell theme={null}
   source <venv_path>/bin/activate
   ```

2. Install the Ultralytics package and export the ONNX model.
   ```shell theme={null}
   pip install ultralytics
   ```
   ```shell theme={null}
   yolo export model=yolov8s.pt imgsz=320 format=onnx opset=11 optimize=True simplify=true
   ```

### Procedure

1. Convert the ONNX model to DLC.
   ```shell theme={null}
   snpe-onnx-to-dlc -i yolov8s.onnx
   ```

2. Generate quantized DLC.

   1. Prepare the calibration data set.
   2. Gather 5-10 images that used during training and save these images in the input directory.
   3. Use the `preprocess.py` script to convert `.jpg` images into the RAW files required for quantization.

      <Note>
        In this example, the model uses an input dimension of 320x320.
      </Note>

      1. Download the script as follows:
         ```shell theme={null}
         wget https://raw.githubusercontent.com/qualcomm/sample-apps-for-qualcomm-linux/refs/heads/main/qualcomm-linux/scripts/preprocess.py
         ```
      2. Run the script with the following options:
         ```shell theme={null}
         python preprocess.py <INPUT PATH> <OUTPUT PATH> 1 0
         ```
         * `<INPUT PATH>`: Folder containing the original images
         * `<OUTPUT PATH>`: Folder where the RAW files will be generated
   4. Create an `input.txt` file containing the paths to all generated RAW files.

      The quantization process needs this file.

3. Quantize the model, using `snpe-dlc-quantize` to convert the model to quantized DLC.
   ```shell theme={null}
   snpe-dlc-quantize --input_dlc yolov8s.dlc --input_list input.txt
   ```

## Run the demo

1. Download the labels file. See [Download model and label files](../topic/classify-objects-with-default-model#download-model-and-label-files).

2. On the host computer, set the user environment variable:
   ```shell theme={null}
   export USER=root
   ```

3. Push the test video file `/etc/media` on the device.
   ```shell theme={null}
   scp <video file> $USER@<IP address of target device>:/etc/media/
   ```

4. Push the quantized YoloV8 model to the device.
   ```shell theme={null}
   scp <model file> $USER@<IP address of target device>:/etc/models/
   ```

5. Retrieve the output tensor of the model, for example, `output0`.
   <img src="https://mintcdn.com/qualcomm-prod/WwC9kmcnKl9Ef7de/Key-Documents/AI-Developer-Workflow/_images/node-output0.png?fit=max&auto=format&n=WwC9kmcnKl9Ef7de&q=85&s=b5a57333db94f3a3c9484c0f230ee051" alt="Model output tensor name in Netron graph viewer" width="1511" height="802" data-path="Key-Documents/AI-Developer-Workflow/_images/node-output0.png" />

6. Sign in to the device using SSH:
   ```shell theme={null}
   ssh $USER@<IP_ADDRESS_OF_TARGET_DEVICE>
   ```

7. In the new shell, run the following commands:
   ```shell theme={null}
   gst-launch-1.0 -e filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split split. ! queue ! qtivcomposer name=mixer ! queue ! waylandsink fullscreen=true sync=true split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/etc/models/yolov8s_quantized.dlc tensors="<output0>" ! queue ! qtimlpostprocess settings="{\"confidence\": 51.0}" results=10 module=yolov8 labels=/etc/labels/yolov8.json ! video/x-raw,width=640,height=360 ! queue ! mixer.
   ```