Edge AI

Hello,First of all, I'm a newbie in STM32N6 boards, so I apologize in advance if my question is fairly basic.I am trying to deploy an application for object detection using the STM32N6570-DK.I am following the "Object Detection STM32N6 Model Deployment" tutorial from stm32ai-modelzoo-services/object_detection/docs/README_DEPLOYMENT_STM32N6.md at main · STMicroelectronics/stm32ai-modelzoo-services · GitHub.In my case, for testing I am trying to deploy the "yolov8n_256_quant_pc_uf_od_coco-person-st_tflite" file as the object detection model in combination with the Python script "st32ai_main.py". I planned to use my own YOLOv8-based object detector with other classes, but first I tried with an available tflite yolov8 model from ST and Ultralytics.Running the s

I want to train some small DL models with Nvidia's RTX 50 series GPU, which can work properly only with CUDA 12.x. I've learned that the CUDA 12.x is only compatible with ONNX Runtime 1.17.x or newer version. So I want to know if the X-CUBE-AI 10.2.0 support ONNX Runtime 1.17.x or newer version? If it can, I would like use the X-CUBE-AI 10.2.0 to deploy my DL models on the STM32 MCUs.

Hi,I have build a custom model based on st yolox model.This model have an input of 480x480. Intput int8 and output float.Five classes for vehicles detectionQuantized and tested on stm32N6570-DK.Now i'm trying to add my model on this st examplehttps://github.com/STMicroelectronics/STM32N6-GettingStarted-ObjectDetection/tree/mainI'm following this tutorial to deploy my modelhttps://github.com/STMicroelectronics/STM32N6-GettingStarted-ObjectDetection/blob/main/README.md#application-build-and-run---dev-modeI'm using stm32cubdeIDE to program the cardAfter i programmed the network_da

Hi,I am working on an STM32N6 project inspired by the peopleDetection example in which I try to run a yolov8 for instance segmentation.My pipeline uses camera (DCMIPP) + LTDC display + NPU inference (ATON / X-CUBE-AI).Each subsystem works correctly on its own:If I comment out the inference code in the main while, camera + LTDC work perfectly.If I comment out the camera/display code and keep only the inference (LED toggle + delay), the LED blinks, meaning NPU inference works correctly.This behavior is consistent when programming through CubeProgrammer.However, when everything is enabled at the same time, the display stays completely black.The portion of code that causes the issue is:LL_ATON_RT_Init_Network(&NN_Instance_Default);/* Run Inference */do{ll_aton_rt

I’m currently testing the system with different configuration options, and I am trying to understand the impact of the --enable-epoch-controller option flag.Could you explain what functional or performance differences occur when this option is enabled versus when it is disabled?What I know so far is that when the option is enabled, the network_ecblob.h file is generated, and when it is disabled, the file is not generated.

Background:I am using the STM32N6570-DK board and trying to use the NPU for acceleration. For now, I want to run the official demo (x-cube-n6-ai-people-detection-tracking-main) to learn and verify the workflow. However, I have encountered some issues:1. When I debug and run the official x-cube-n6-ai-people-detection-tracking-main example directly in CubeIDE, the screen turns on but it only shows a gray display, as if the program is blocked or stuck somewhere.2. I have already programmed the network_data-dk.hex file to the board using STM32CubeProgrammer.I would like to know why the demo is not running correctly and what might be causing the program to get stuck.

Dear ST staff, Recently, while using STM32CubeMX with CubeAI to deploy the yolov8n_192_quant_pc_uf_od_coco-person.tflite model from object_detection onto the STM32N6570-DK development board.        Subsequently, following CubeAI's configuration, I programmed the network_atonbuf.xSPI2.bin file into the 0x71000000 memory location

Hey!I have a ResNet model that I have deployed on my NUCLEO-N657X0-Q, and I want to run a training step on the board itself. I'm fine even with training just the last 1-2 layers of my model on a single sample, so that the memory on the board will suffice. Is the NPU itself capable of it? is there a assisting tool for on-board training? Is it more likely to succeed by using the ARM cpu for that?Thank you,Amir

Dear ST Team, I'm trying to deploy models on STM32N6570-DK, and there is the issue:Followed tutorial below:stm32ai-modelzoo-services/object_detection/docs/README_DEPLOYMENT_STM32N6.md at main · STMicroelectronics/stm32ai-modelzoo-servicesEncountered error message below:(st_zoo) PS C:\Users\ricky\git\stm32ai-modelzoo-services\object_detection> python stm32ai_main.py --config-path ./src/config_file_examples/ --config-name deployment_n6_ssd_mobilenet_v2_fpnlite_config.yaml C:\Users\ricky\anaconda3\envs\st_zoo\lib\site-packages\mlflow\utils\requirements_utils.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_reso

Hello,I have a solution which I am trying to port to STM32 MCUs which are able to run a machine learning/deep learning model. i wanted to know is it possible to generate a combined network.c from two models? Or do I have to generate multiple network.c files like network1.c, network2.c etc.How does it work? I have more than one(2-3) DCNN models.please adviceregards, 

Hello ST team,I am currently trying to deploy an rPPG (remote photoplethysmography) model to the STM32N6 using the ST Edge AI Developer Cloud.My model’s input tensor has the following 6-dimensional shape:(B, 8, 3, 36, 36, 3)B = batch size8 = temporal frames3 = feature channels36x36 = spatial resolution3 = RGB channels

Good day Folks, Firstly, is it possible to put NN weights on the external Flash, but not use it in memory mapped mode? BSP_XSPI_NOR_Init_t NOR_Init; NOR_Init.InterfaceMode = BSP_XSPI_NOR_OPI_MODE; NOR_Init.TransferRate = BSP_XSPI_NOR_DTR_TRANSFER; BSP_XSPI_NOR_Init(0, &NOR_Init); BSP_XSPI_NOR_EnableMemoryMappedMode(0); Secondly is it possible to run an AI model with NO flash access at all, i.e. to only use internal RAM for the case of a small model? I have an application that is already using the flash for storing data such as Logs & settings, via a XSPI Read/Write interface. However that interface is broken when configuring the flash to use the memory map.    The image classification STM32N6570-DK example code runs fine and can use the external flash, however it then appears I can't use the Flash for anything else but the NN tables & weights. I then tried

Device : STWIN.Box (steval stwbxcs1a) with DATALOG2 3.1.0 firmware. WIFI : Updated the MXCHIP firmware   Objective: Running WiFi connection with APP and/or PnPL messages, Data retrieving with FTP.   Currently this is my application  as viewed in ST BLE Sensor app (iOS)

Hi,i'm currently working on a school project with the STM32n6570-DK and I'm pretty new into the STM scene. So my project will mainly be an object detection of different objects. The question is not directly about the STM, it's more about how to implement YOLO into STM. I heard something about the stm32-ai-model-zoo but I didn't go to deep into it yet. But is it possible to train a custom dataset with e.g. Google Colab and then implement it into STM or do I need a specific model-zoo file? And last but not least if someone has experience with STM32n6570-DK connected with YOLO, which YOLO version should I use? I heard YOLOv8n isn't that bad but I wanted to hear some more opinions. Hopefully someone can help me.Thanks for reading!Best Wishes

Hey!I'm trying to run my AI model on my NUCLEO-657X0-Q, following @Julian E. great guide in here.I have 2 questions:1. My model has some running issues during booting.In the FSBL code that suppose to jump to the application (JumpToApplication) there is some crash between the start of __set_MSP() and the start of _set_PRIMASK(), I would say it maybe an error in the pointer to the application code, but when I run it step by step in the IDE's debugger it works just fine.Do you have an idea of why would that happen / how to debug it? (I don't have printings at this stage, and turning in the LEDs in the middle of stack switch makes it fail in the debugger too).

Hello,Im trying to use the stedgeai generate command and want to move the data that gets located in the octoflash which seems to default to name=octoFlash offset=0x71000000 at times. Id like to relocate it at another position in the octoflash because i need to run several models. I tried adding the following that I found in an example and the output does not change at all from running it with the following options or not:--binary --address 0x810000or --binary --address 0x810000 --copy-weights-at 0xD0000000I found these examples via a search but as I said they dont seem to change the output.What am i doing wrong, I would like to change the default 0x71000000 to say something like 0x73000000, how can i accomplish this? I also tried to just change that in the network.c file, that didnt go well, just locked up the project at run time.  Thanks

Hello STMicroelectronics Community,I’m excited to engage with this platform and explore the wealth of resources available here. STMicroelectronics has long been at the forefront of innovation in semiconductors and embedded systems, and this community is a great space to exchange knowledge, ask questions, and collaborate with experts.From product forums and knowledge bases to learning materials and developer news, the community provides essential tools for both beginners and experienced professionals. I’m particularly interested in exploring:Latest product updates and development toolsBest practices shared in the knowledge base and FAQsLearning opportunities for embedded systems, IoT, and microcontrollersNetworking and collaboration with other developersLooking forward to contributing to discussions, sharing insights, and learning from the expertise within this community.Thank you for

Hi I am using the STM32N6570 with AI application models. I have seen the STM Model Zoo and STM32 AI Services. In the services, the application code is generated automatically based on the model selected from the Model Zoo. My question is: how do I develop my own AI application from scratch in STM32CubeIDE, without using the Python scripts? I have already run the Python scripts and they work fine, but I want to know how to build the application manually from scratch in CubeIDE.

Dear STEnvironmentItem Version / Details BoardSTM32N6570-DKOSUbuntu 24.04 LTSPythonConda, Python 3.10STM32AI Model Zoostm32ai-modelzoo-services/object_detectionOperation modedeploymentModelyolov8n_256_quant_pc_uf_od_coco-person-st.tfliteSTM32CubeIDE1.19.0ST Edge AI Core / STM32CubeAI10.2.0Local tool/opt/ST/STEdgeAI/2.2/Utilities/linux/stedgeaiDescriptionI am using the STM32AI Model Zoo deployment workflow on Linux to deploy a YOLOv8 object detection model to the STM32N6570-DK.The deployment pipeline reaches the final flashing stage and abort

Hello, I am working with the STM32N6570‑DK discovery kit and using the ST Edge AI Developer Cloud to generate code for my neural network. The tool produces a project with multiple files (model, runtime, headers, etc.), but I am struggling to understand how to actually integrate this into my STM32N6 application.Specifically, I would like to know:What is the expected input format (tensor dimensions, quantization type) and how do I feed my sensor/camera data into it?How do I retrieve the output from the inference (classification vector, bounding boxes, etc.) and use it in my application logic?How should I connect the generated code with my application (main loop, data acquisition from camera/audio, etc.)?I have looked through the STM32N6 examples an

Hello,I am trying to test a deployed application for object detection.I have tested out of the box object detection application code.I can successfully build the project and I can run the code on my board.Unfortunately it doesn't detect any objects. It is supposed to detect person but it never detected.I have tried to use different pre-trained models and built and run them successfully but none of them work.The screen shows the live camera image, shows inference value and detected object number is always zero.Is there anything that I miss? By the way, the factory demo AI application works perfectly, it successfully detects people. So the hardware is OK.

Hi,I’m working on benchmarking and deploying a 33 MB half-INT8 model. During benchmarking, the measured inference time in ST dev cloud was about 264 ms. However, after compiling the model locally using STEdgeAI and deploying it, the inference time was found to be 405 ms.Additionally, when using the “Generate Code” option in the ST Dev Cloud, the generated artifacts—when deployed—showed the same inference time of 405 ms.I have verified that the weight and activation memory allocations are consistent with those used in the ST Dev Cloud (please refer to the attached logs).Could you please confirm whether this difference in inference time is expected, or if there is a known delta that should be addressed?logs:ST Dev cloud:>>> stedgeai analyze

Hello,I am trying to run a Transformer model with Multi-head self attention on the STM32N6570-DK board.The model is this one : https://github.com/arx7ti/cold-nilmHere is what I did so far:Convert the .ckpt I got from the above repo into a .onnx format.Import and Quantize the model using your ST Edge AI Developer Cloud tool.Try to use stedgeai `generate` command:From the standalone tool on my Linux machineFrom STCubeMX plugginFrom the ST Edge AI Developer Cloud--> None of thoses attempts were succesful. Here is the command I used in the first case:stedge

I’m currently testing my own quantized model on the STM32N6570-DK, using the official X-CUBE-AI application example.All Model Zoo examples (e.g. SSD, image classification) run perfectly with the same firmware and codebase.However, when I replace the model with my own quantized TensorFlow Lite model (Zero-DCE, converted with ST Edge AI Developer Cloud), the inference process freezes — even though the conversion and compilation complete without errors.I’m wondering if there is a hard restriction that prevents models from running when used RAM exceeds the on-chip SRAM (≈ 4.2 MB), even though the report shows that part of the activations are mapped to external HyperRAM.Here are the key parts of my Edge AI Core 2.2.0 generation report:ST Edge AI Core v2.2.0-20266Model name: zerodce_int8_192Total memory usage:----------------------------------------------Total: 11.643 MBWeights: 78.751 KBActivations: 11.566