object_detection/docs/README_MODELS.md

Overview of object detection STM32 model zoo

The STM32 model zoo includes several models for object detection use case pre-trained on custom and public datasets. Under each model directory, you can find the following model categories:

• Public_pretrainedmodel_public_dataset contains public object detection models trained on public datasets.
• ST_pretrainedmodel_custom_dataset contains different object detection models trained on ST custom datasets using our training scripts.
• ST_pretrainedmodel_public_dataset contains different object detection models trained on various public datasets following the training section in STM32 model zoo.

Feel free to explore the model zoo and get pre-trained models here.

Model Families

You can get comprehensive footprints and performance information for each model family following the links below.

STMicroelectronics In-house Model

• st_yololcv1 - This is an in-house optimized version of the tiny YOLO v2 object detection algorithm. It was optimized in-house to work well on embedded devices with limited computational resources.
• st_yoloxn - Unlike its predecessors, YOLOX can adopt an anchor-free approach, which simplifies the model and enhances its accuracy. It also incorporates advanced techniques such as decoupled head structures for classification and localization, and a more efficient training strategy. YOLOX is designed to achieve high accuracy and speed, making it suitable for real-time applications in various computer vision tasks. This ST variant embeds various tuning capabilities from the yaml configuration file.

Standard Architectures

• yolov2t - This is a lightweight version of the original YOLO (You Only Look Once) object detection algorithm targetting embedded devices with limited computational resources.

• yolov8n - This is an advanced object detection model from Ultralytics that builds upon the strengths of its predecessors in the YOLO series. YOLOv8 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.

• yolov11n - This is an even more recent variant of the YOLO family from Ultralytics. Topology is improved for efficiency, speed, accuracy with lower number of parameters.

• face_detect_front - BlazeFace Front 128x128 is a lightweight and efficient variant of the BlazeFace architecture, designed specifically for detecting frontal faces at a resolution of 128x128 pixels

• yolov4 - Version 4 from Nvidia TAO train adapt and optimize toolkit with different possible backbones for feature extractions including different versions of resnet, mobilenet, darknet, efficientnet, vgg, and cspdarknet

• yolov4t - Version 4 tiny from Nvidia TAO train adapt and optimize toolkit with backbones csp_darknet_tiny as the backbone

To get started, update the user_config.yaml file, which specifies the parameters and configuration options for the services you want to use. The model section of this YAML specifically relates to the model definition. Some topologies are already registered and can be accessed by the model_type and model_name attribute.

The exhaustive list of model_type and corresponding model_name attributes currently supported for the object detection are listed herafter.

model_type	possible model_name
yolov8n	X
yolov11n	X
yolov5u	X
st_yoloxn	st_yoloxn, st_yoloxn_d033_w025, st_yoloxn_d100_w025, st_yoloxn_d050_w040
st_yololcv1	st_yololcv1
yolov2t	yolov2t
yolov4	X
yolov4t	X
face_detect_front	X

When no model_name attribute is possible, model_path is to be used.

Feel free to explore the model zoo and get pre-trained models here. For training and deployment guidance, refer to the STM32 AI model zoo documentation.