Instance segmentation STM32 model zoo
January 22, 2026 ยท View on GitHub
Directory Components:
- datasets placeholder for the instance segmentation datasets.
- docs contains all readmes and documentation specific to the instance segmentation use case.
- src contains tools to do predictions, benchmark or deployment of your model on your STM32 target.
Quick & easy examples:
The operation_mode top-level attribute specifies the operations or the service you want to execute. This may be single operation or a set of chained operations.
You can refer to readme links below that provide typical examples of operation modes, and tutorials on specific services:
All .yaml configuration examples are located in config_file_examples folder.
The different values of the operation_mode attribute and the corresponding operations are described in the table below:
| operation_mode attribute | Operations |
|---|---|
prediction | Predict the classes some images belong to using a float or quantized model |
benchmarking | Benchmark a float or quantized model on an STM32 board |
deployment | Deploy a model on an STM32 board |
The model_type attributes currently supported for the instance segmentation are:
yolov8n_seg: is an advanced instance segmentation model from Ultralytics that builds upon the strengths of its predecessors in the YOLO series. It is designed for real-time segmentation, offering high IoU and speed. It incorporates state-of-the-art techniques such as improved backbone networks, better feature pyramid networks, and advanced anchor-free detection heads, making it highly efficient for various computer vision tasks.
You don't know where to start? You feel lost?
Don't forget to follow our tuto below for a quick ramp up :
Remember that minimalistic yaml files are available here to play with specific services, and that all pre-trained models in the STM32 model zoo are provided with their configuration .yaml file used to generate them. These are very good starting points to start playing with!