Edge AI

hello,I want to train using Model Zoo with a custom dataset. I'm trying to convert the data from COCO format to Darknet format, but I encountered an error with the YAML file (see attached).

We reported the following error during the "optimize" step of generating an ONNX model: (ST Edge AI Core v1.0.0-19894  NOT IMPLEMENTED: Unsupported layer types: ScatterND).How to solve this issue? Is there any function or tool that can replace ScatterND? 

hello,I want to deploy an object detection application on the STM32H747 board. I used YOLOv5n; can I use FP-AI-Vision like in the example from the tutorial "How to use transfer learning to perform image classification on STM32".Best Regard

Hello,I have STM32H7S78-DK board and its have ARM cortex M7 core. As per research I saw that AI model is work on the same M7 cortex but I didn't know how it will work? would you please let us know STM32H7S78 supports AI model implements or not and if yes then can anyone provide steps to how we can implements on the board. Thanks.

I was trying to reconfigure and customize the HMI given by HSDATALOG2 interface , i have been looking into utilities/HSDPython_SDK/examples ; utilities/HSDPython_SDK/st_dtdl_gui but i can't see how i can manage the controll over the widgets colours and texts . i' ve consulted the scripts written in python , JSON files but nothing changes while executing the main . is there a file that contains all the widgets , how may i make my own changements.       @SimonePradolini

Hello everyone,I am learning how to use the ST Edge AI Developer Cloud tool and, after downloading my model that I developed with TensorFlow and selecting the STM32 MCUs platform, I wanted to try the post-training quantization but I encountered the following error:Executing with: {'model': '/tmp/quantization-service/970d7e77-a0e3-4d23-a8ec-5e7e52d9a260/1dcnnrnn_01.h5', 'data': '/tmp/quantization-service/970d7e77-a0e3-4d23-a8ec-5e7e52d9a260/dataset4quantization.npz', 'input_type': tf.float32, 'output_type': tf.float32, 'optimization': <Optimize.DEFAULT: 'DEFAULT'>, 'output': '/tmp/quantization-service/970d7e77-a0e3-4d23-a8ec-5e7e52d9a260', 'disable_per_channel': False} Error when deserializing class 'InputLayer' using config={'batch_shape': [None, 9999, 1], 'dtype': 'float32', 'sparse': False, 'name': 'input_layer_4'}. Exception encountered: Unrecognized keyword arguments: ['batch_shape']The error occurs regardless of weather I provide a data set fo

I want to implement air gesture recognition using accelerometer and gyroscope data. With different movements you should be able to make settings on the handheld device. Movements to be recognized are, for example, tipping twice or moving up and down twice. My findings so far:The first attempt was made with LSM6DSO and it's Finite State Machine (FSM) by using Unico tool. Basically it works, but the reliability is too low. The second attempt was made with LSM6DSOX and it's Machine Learning Core (MLC) by using Unico tool. Reliability is too low in this case as well.The third attempt was made with Qeexo AutoML using the SensorTile Box MCU for processing the sensor data. Similar to this description. 

Hello,I am using the model from 'STM32 AI Model Zoo' located at 'stm32ai-modelzoo/image_classification/pretrained_models/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_tl' for image classification. I have installed and tested the Device Application using the Template Application in X-CUBE-AI 9.0.0.I verified that the input data is correctly received, and I understand that the output should be quantized integer values according to the quantization results. However, I have found that all the output values in the first tensor of the model's data_outs are 0.- 1. Can this happen if I don't use the stm32h747i board?- 2. Did I misunderstand and write the post process function incorrectly?Thanks

When I use multiple networks in one program, I get the following errorAI platform (API 1.1.0 - RUNTIME 8.1.0)Discovering the network(s)...Found network "network_fe"Creating the network "network_fe"..Initializing the networkE: AI error (ai_mnetwork_init) - type=0x30 code=0x13 

Hello,Please, I would like to know if Edge Impulse is compatible with the STM32H747-DISCO, and more specifically, if the FOMO model can be deployed in this board.Best regards

Hello,I would like to run a ML model which I had created using tflite on the STM32H7 series. I have installed x-cube-ai 8.1. Are there any tutorials or manuals where I can understand the functions to be used.There are a few videos but those are the old ones and I am getting errors while using those functions / modules.Can anyone please help me with this?-RegardsHrishikesh

Hello,I want to implement an application for detecting items in the kitchen on an STM32-H747i-DISCO board. I installed Python 3.10.2, TensorFlow 2.8, CUDA 11.7, and cuDNN v8.8.1. After installing the model zoo , I tried running the command in  Command Prompt python stm32ai_main.py , from this path " ..\stm32ai-modelzoo\object_detection\src".  But I encountered the attached errorI

I want to run Cube AI Model zoo examples on STM32H745. The current example in Model zoo is for STM32H747 with a display, I can replace the display and print the output on UART.  Is there an example or tutorial that explains the changes required to run AI on STM32H745? I would appreciate a detailed answer with a guide, as I am new to STM MCUs. Development kits: NUCLEO-144 STM32H745ZI EVAL BRD

im trying to run a model that distuingish pasta types using stm32h747i and camera . i followed this guide  https://wiki.stmicroelectronics.cn/stm32mcu/index.php?title=AI:How_to_use_Teachable_Machine_to_create_an_image_classification_application_on_STM32&sfr=stm32mcu to be short , i followed every step and the model worked .  but when u tried to run the project the next day the camera didnt work properly .  i tried other projects and the camera work fine  its just in this project any help? i should mention that i didnt follow the part where i need   to change the flipmirror function because its mot clear.  but the camera worked properly the first time without doing that part .&nbsp

It seems like a used ram size fit to External SDRAM in STM32H757i- EVAL Board.However, I can see the error when doing analyze.Also, I cannot see any TFLite Micro runtime generated code.I can see empty function.I was totally fine with STM32CUBE AI runtime. 

How to figure out which model can be deployed on the board, according to the available RAM on the board.The ST-yolo-LC model from the ST model zoo seems to fit the board's RAM availability,(i) is it possible to train this model for my own usecase i.e fall detection and implement the same?(ii) And if I retrain the model for my own use, can I still use the application as it is from the AI function pack examples.(iii) What model is st-yolo-LC, is it a particular of yolo models or a custom model?

When I used validation app I could see like  notes: - the output of the reference model is used as ground truth/reference value         - 10 samples (5 items per sample)   acc=100.00%, rmse=0.000000147, mae=0.000000066, l2r=0.000000430, nse=1.000, cos=1.000   5 classes (10 samples)   ---------------------------------   C0        0    . &n

Hi,I'm encountering an issue while porting a trained TensorFlow-Lite model, obtained from a research paper, onto a NUCLEO-F401RE board. The process goes smoothly when analyzing and validating the code on my desktop, generating the code, and downloading it to the board. However, when I attempt to perform a "validate of target," I receive an "Invalid firmware" error (screenshot attached). The model is neural network based on U-Net architecture with shallow structure, single-channel input, and output.Board used: NUCLEO-F401RESTM32MX : Version 6.12.0STM32CubeIDE : Version: 1.16.0Could you please provide guidance on how to resolve this issue? Any suggestions for modifications or troubleshooting steps would be greatly appreciated.Thank you!

I wanted to share some feedback regarding the use of ModelZoo, as you previously recommended. Unfortunately, after dedicating significant time—about a month—to working with it, I have encountered numerous challenges that have made it difficult to achieve the intended outcomes.The errors and bugs I've faced have been persistent, and despite consulting with various ST employees and even the ST community, I have not been able to resolve them. It appears that the structure of the ModelZoo folder differs significantly from what is presented in the tutorials, rendering the tutorials largely unhelpful. Additionally, the README files provided do not offer the necessary guidance to overcome these issues.One particular area of concern is the YAML files, which do not align with those shown in the tutorial. This discrepancy has led to repeated errors, including proxy errors, that are not addressed or explained in the existing documentation.Given these difficulties, I believ

I have a STEVAL-PROTEUS device that I collected data with and NanoEdgeAI returned a model that has very high accuracy (almost 100%). In fact, during the validation step on NEAI, the emulator returns very accurate results as well. However, in the deployment step, after compilation (I left everything as default but these are the settings), and replacing the the NCC stub library provided in FP-AI-PDMWBSOC, the results are very poor and totally unlike the results from the emulator.Has anyone seen this or has any idea what might be wrong?

Hi everyone,I am using version 4.5.0 of Nano Edge AI with the STEVAL-PROTEUS1 sensor kit. I am trying to implement anomaly detection for motor vibration. However, when I attempt to add a signal using USB (serial), I occasionally receive meaningless 100-line samples, and sometimes I do not receive any samples at all. Does anyone have an idea of what might be causing this issue? 

In CubeMX I have installed the AI package. After that I go to "Start My project from MCU" then I go to "Middleware" then "Artificial Intelligence" and Enable it.Here, I select whatever model and click Analyze. The button becomes grey, but I don't get any error or other message. According to the videos I saw online, it should analyze the model and compress it.  For the pre-trained model, I tried my own models and the models from the "stm32ai-model zoo" git repository. The Runtime I want to use is "STM32Cube.AI MCU". I have tried all the combinations, and all the models in the repository have the same results. stm32ai-model zoo = \stm32ai-modelzoo\object_detection\pretrained_models\   

I have a large PyTorch model that I pruned using unstructured methods to decrease its size. The model size can only decrease if I filter out the zeros and save the model in a sparse format.However, my problem is that ONNX doesn't support the sparse format. Does anyone know how I could prune a model, filter out the zeros to reduce the model size, and implement it on an evaluation board?  

How to input validation image dataset in the stm32cubeMX.    

When I try to launch the "train.py" file, in conda virtual environment it gives me an error "ModuleNotFoundError: No module named 'logs_utils'". This is a module called on the code line 23 of the train.py. After that I've proceed with a "pip list" to see the modules instaled, but indeed the 'logs_utils'" isn't there.I've tried to install it with "pip install logs_utils" but it simply can't find the module.