Yolov8_n deployment on X-CUBE-N6-AI people detection → black screen

HarisHer · ‎2025-08-29

Hi,

After deploying Yolov8_n to x-cube-n6-ai-people-detection app stops working, I only get a black screen.

I generated the TFLite model
using the following script:

from ultralytics import YOLO

# load model
model = YOLO("yolov8n.pt")

# export to TFLite
model.export(format="tflite", imgsz=320, int8=True, nms=False)

Then I took:
yolov8n_full_integer_quant.tflite

and ran:

stedgeai generate --no-inputs-allocation --no-outputs-allocation \
  --model yolov8n_full_integer_quant.tflite \
  --target stm32n6 \
  --st-neural-art default@user_neuralart.json

cp st_ai_output/network_ecblobs.h .
cp st_ai_output/network.c .
cp st_ai_output/network_atonbuf.xSPI2.raw network_data.xSPI2.bin

arm-none-eabi-objcopy -I binary network_data.xSPI2.bin \
  --change-addresses 0x70380000 -O ihex network_data.hex

After that I flashed the data:

STM32_Programmer_CLI.exe -c port=SWD mode=HOTPLUG -el "$env:DKEL" -hardRst -w network_data.hex

Inside CubeIDE, when I run the app it is extremely slow and I never see frames from the camera.

app config

postprocess config

When I do the same with st_yolo_x_nano_480_1.0_0.25_3_int8 and set:

#define POSTPROCESS_TYPE POSTPROCESS_OD_ST_YOLOX_UF

everything works perfectly well.

Has anyone managed to run Yolov8_n on the STM32N6570-DK with people/vehicle detection?
Any hints on postprocess config, arena/memory pool size, or model export settings would be greatly appreciated.

Thanks in advance!