2026-05-07 1:23 AM
Hello,
I am trying to deploy a custom image classification model (bacteria classification) on the NUCLEO-N657X0-Q board using the STM32 AI Model Zoo v4.0.0 deployment pipeline.
After flashing the generated files to the board, the firmware does not boot — no serial output (tested at 115200 and 921600 baud), no USB UVC camera device appears in Windows Device Manager.
This confirms the issue is specifically with the signed application binary, not with the model weights or FSBL.
Signing command used:
"C:\Program Files\STMicroelectronics\STM32Cube\STM32CubeProgrammer\bin\STM32_SigningTool_CLI.exe" -s -bin UVCL_ModelZoo\NUCLEO-N657X0-Q_GettingStarted_ImageClassification_noecblob.bin -nk -t ssbl -hv 2.3 -o UVCL_ModelZoo\NUCLEO-N657X0-Q_GettingStarted_ImageClassification_signed.bin
Output:
[Header v2.3] Do not align the payload to the 0x400 offset, No padding bytes at the beginning of the payload.
Header version 2.3 preparation...
Extracting Entry point value from the input file...
Entry point value : 0x3401e679
The header image file generated successfully
When running the deployment with STM32_Programmer_CLI in PATH and board connected, the CLI fails with:
Error: failed to erase memory
Erasing external memory sectors [16 20]
Should I use a different header version (-hv) for the signing?
bacteria_deploy_n6.yaml:
model:
model_path: <path_to>/converted_model_quant_qdq_pc.onnx
operation_mode: deployment
dataset:
dataset_name: custom_dataset
class_names: [Candida_albicans, Clostridium_perfringens, Escherichia_coli, Lactobacillus_plantarum, Staphylococcus_aureus]
preprocessing:
resizing:
interpolation: bilinear
aspect_ratio: fit
color_mode: rgb
tools:
stedgeai:
optimization: balanced
on_cloud: True
path_to_stedgeai:
path_to_cubeIDE: C:/ST/STM32CubeIDE_1.18.1/STM32CubeIDE/stm32cubeide.exe
deployment:
c_project_path: ../application_code/image_classification/STM32N6/
IDE: GCC
verbosity: 1
hardware_setup:
serie: STM32N6
board: NUCLEO-N657X0-Q
output: UVCL
hydra:
run:
dir: ./tf/src/experiments_outputs/${now:%Y_%m_%d_%H_%M_%S}
mlflow:
uri: ./tf/src/experiments_outputs/mlruns
Thank you for any help!