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This guide explains how to modify an existing Qualcomm IM SDK reference application to work with a custom-trained model. It uses a custom-trained YOLOv8 model as an example.

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Use a custom-trained YOLOv8 LiteRT model

Qualcomm IM SDK reference applications use the YOLOv8 model for object detection. This example explains how to run a custom-trained YOLOv8 variant with the current reference application. To run your own custom-trained YOLOv8 model, complete the following steps:
  1. Replace the existing model with your new model in the reference application.
  2. Update the label files with your custom labels.
  3. Run the reference application with the updated model.

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Update the label files

The Qualcomm IM SDK reference applications expect labels in JSON format. Update the id, color, and label values for each entry in the labels file. The format for each label entry is: 
[
  {"id": 0, "color": "0x00FF00FF", "label": "person"}
]
For example: 
[
  {"id": 0, "color": "0x00FF00FF", "label": "person"},
  {"id": 1, "color": "0x00FF00FF", "label": "bicycle"},
  {"id": 2, "color": "0x0000FFFF", "label": "car"},
  {"id": 3, "color": "0x00FF00FF", "label": "motorcycle"}
]

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Run object detection with the custom model

To run object detection using the LiteRT runtime with your custom model and label files, complete the following steps:
  1. On the host computer, set the user environment variable: 
    export USER=root
  2. Copy the model to the device: 
    scp yolov8_custom.tflite $USER@<IP_ADDRESS_OF_TARGET_DEVICE>:/etc/models/
  3. Copy the label file to the device: 
    scp yolov8_custom.json $USER@<IP_ADDRESS_OF_TARGET_DEVICE>:/etc/labels/
  4. Sign in to the device using SSH: 
    ssh $USER@<IP_ADDRESS_OF_TARGET_DEVICE>
  5. Edit the /etc/configs/config_detection.json configuration file: 
    {
  "file-path": "/etc/media/video.mp4",
  "ml-framework": "tflite",
  "yolo-model-type": "yolov8",
  "model": "/etc/models/yolov8_custom.tflite",
  "labels": "/etc/labels/yolov8_custom.json",
  "threshold": 40,
  "runtime": "dsp"
}
  6. Run the sample application: 
    gst-ai-object-detection --config-file=/etc/configs/config_detection.json

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Notes

  • To display all available options, run: 
    gst-ai-object-detection -h
  • To stop the application, press Ctrl+C.