Model Export

The IM SDK download script (download_artifacts.sh) provides only 10 base TFLite models. The following demos require custom-exported models:

Demo	Required Model	Export Method
Multi-Input Multi-Output	YOLOv5 (yolov5.tflite)	Export from YOLOv5 source
Multistream Batch Inference	YOLOv8 batch=4 (yolov8_det.tflite)	Qualcomm AI Hub
Object Detection (YOLOv8/YOLO-NAS)	YOLOv8 / YOLO-NAS	Qualcomm AI Hub

Method 1: Export from YOLOv5 Source (for Multi-Input Multi-Output)

host$

git clone https://github.com/ultralytics/yolov5.git
cd yolov5
python -m pip install -r requirements.txt tensorflow-cpu
python export.py --weights yolov5m.pt --img 320 --include tflite --int8 --data data/coco128.yaml

Push the exported model to the device:

host$

scp yolov5m-int8.tflite radxa@<device-ip>:/etc/models/yolov5.tflite

radxa@airbox$

# Create yolov5 label file (copy yolonas labels)
sudo cp /etc/labels/yolonas.labels /etc/labels/yolov5.labels

This method uses --img 320 to match the demo's input resolution. Do not use the default 640, or inference will fail due to input size mismatch.

Method 2: Qualcomm AI Hub Export for YOLOv8 (Object Detection, Multistream Batch)

1. Register and Get API Token

Qualcomm AI Hub → Settings → API Token.

2. Install qai-hub-models

host$

python -m venv qaihm
source qaihm/bin/activate
pip install qai-hub-models

3. Export Standard Model (batch=1)

host$

python -m qai_hub_models.models.yolov8_det.export \
    --quantize w8a8 \
    --target-runtime=tflite \
    --device "Dragonwing IQ-9075 EVK"

4. Export Batch Model (batch=4, for Multistream Batch Inference)

host$

python -m qai_hub_models.models.yolov8_det.export \
    --quantize w8a8 \
    --target-runtime=tflite \
    --device "Dragonwing IQ-9075 EVK" \
    --batch-size 4

--batch-size 4 must match the number of streams in the demo. Q900 (QCS9075) supports up to 4 streams.

5. Push to Device

host$

scp yolov8_det.tflite radxa@<device-ip>:/etc/models/