Welcome to STM32 model zoo!
The STM32 AI model zoo is a collection of reference machine learning models that are optimized to run on STM32 microcontrollers. Available on GitHub, this is a valuable resource for anyone looking to add AI capabilities to their STM32-based projects.
- A large collection of application-oriented models ready for re-training
- Pre-trained models on reference datasets
Scripts to easily retrain, quantize, evaluate or benchmark any model from user datasets as well as application code examples automatically generated from user AI model can be found in the stm32ai-modelzoo-services GitHub
These models can be useful for quick deployment if you are interested in the categories that they were trained. We also provide training scripts to do transfer learning or to train your own model from scratch on your custom dataset.
The performances on reference STM32 MCU, NPU and MPU are provided for float and quantized models.
3.0:
- Included additional models compatible with the STM32N6570-DK board.
- Expanded models in all use cases.
- Expanded use case support to include
Instance SegmentationandSpeech Enhancement. - Added
Pytorchsupport through the speech enhancement Use Case. - Model Zoo hosted on Hugging Face
2.1:
- Included additional models compatible with the STM32MP257F-EV1 board.
- Expanded use case support to include
Pose EstimationandSemantic Segmentation.
2.0:
- An aligned and
uniform architecturefor all the use case
| Use Case | Quick definition | Suitable Targets for deployment | Smart example |
|---|---|---|---|
| Image Classification | Classifies the content of an image within a predefined set of classes. | STM32H747I-DISCO NUCLEO-H743ZI2 STM32MP257F-EV1 STM32N6570-DK |
|
| Object Detection | Detects, locates and estimates the occurences probability of predefined objects from input images. | STM32H747I-DISCO STM32MP257F-EV1 STM32N6570-DK |
|
| Pose Estimation | Detects key points on some specific objects (people, hand, face, ...). | STM32MP257F-EV1 STM32N6570-DK |
|
| Semantic Segmentation | Associates a label to every pixel in an image to recognize a collection of pixels that form distinct categories. | STM32MP257F-EV1 STM32N6570-DK |
|
| Instance Segmentation | Associates a label to every pixel in an image to recognize a collection of pixels that form distinct categories or instances of each category. | STM32N6570-DK |
|
| Audio Event Detection | Detection of a specific audio events. | B-U585I-IOT02A ThreadX B-U585I-IOT02A FreeRTOS STM32N6570-DK |
|
| Speech Enhancement | Enhancement of the audio perception in a noisy environment. | STM32N6570-DK |
|
| Human Activity Recognition | Recognizes various activities like walking, running, ... | B-U585I-IOT02A |
|
| Hand Posture Recognition | Recognizes a set of hand postures using Time of Flight (ToF) sensor | NUCLEO-F401RE |
The Model Zoo Dashboard is hosted in a Docker environment under the STMicroelectronics Organization. This dashboard is developed using Dash Plotly and Flask, and it operates within a Docker container. It can also run locally if Docker is installed on your system. The dashboard provides the following features:
• Training: Train machine learning models. • Evaluation: Evaluate the performance of models. • Benchmarking: Benchmark your model using ST Edge AI Developer Cloud • Visualization: Visualize model performance and metrics. • User Configuration Update: Update and modify user configurations directly from the dashboard. • Output Download: Download model results and outputs.
You can also find our models on Hugging Face under the STMicroelectronics Organization. Each model from the STM32AI Model Zoo is represented by a model card on Hugging Face, providing all the necessary information about the model and linking to dedicated scripts.
The model zoo repo is using the git lfs, so the users need to install and set up the git lfs before cloning the repo. This can be done by following the instructions below.
-
On Ubuntu:
sudo apt-get install git-lfs
-
On Windows: Download the Git LFS extension here.
Once downloaded and installed, set up Git LFS for your user account by running the following command:
git lfs installYou should see the message Git LFS initialized. if the command runs successfully.
NOTE: If you do not see the message Git LFS initialized., visit the GitHub documentation page for more details and support.
git clone https://github.com/STMicroelectronics/stm32ai-modelzoo.git