Yolov8n_seg

Use case : Instance segmentation

Model description

Yolov8n_seg is a lightweight and efficient model designed for instance segmentation tasks. It is part of the YOLO (You Only Look Once) family of models, known for their real-time object detection capabilities. The "n" in Yolov8n_seg indicates that it is a nano version, optimized for speed and resource efficiency, making it suitable for deployment on devices with limited computational power, such as mobile devices and embedded systems.

Yolov8n_seg is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter.

Network information

Network Information	Value
Framework	Tensorflow
Quantization	int8
Paper	https://arxiv.org/pdf/2305.09972

Recommended platform

Platform	Supported	Recommended
STM32L0	[]	[]
STM32L4	[]	[]
STM32U5	[]	[]
STM32MP1	[]	[]
STM32MP2	[x]	[]
STM32N6	[x]	[x]

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Performances

Metrics

Measures are done with default STEdgeAI Core version configuration with enabled input / output allocated option.

All YOLOv8 hyperlinks in the tables below link to an external GitHub folder, which is subject to its own license terms: https://github.com/stm32-hotspot/ultralytics/blob/main/LICENSE Please also check the folder's README.md file for detailed information about its use and content: https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/README.md

Reference NPU memory footprint based on COCO dataset

|Model | Dataset | Format | Resolution | Series | Internal RAM (KiB)| External RAM (KiB)| Weights Flash (KiB) | STEdgeAI Core version | |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------------------------| | Yolov8n seg per channel | COCO | Int8 | 256x256x3 | STM32N6 | 855 | 0.0 | 3393.42 | 3.0.0 | | Yolov8n seg per channel | COCO | Int8 | 320x320x3 | STM32N6 | 1413.89 | 0.0 | 3435.34 | 3.0.0 |

Reference NPU inference time based on COCO Person dataset

| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------| | YOLOv8n seg per channel | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 31.57 | 29.72 | 3.0.0 | | YOLOv8n seg per channel | COCO-Person | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 41.87 | 22.83 | 3.0.0 |

Reference MPU inference time based on COCO 2017 Person dataset (instance segmentation)

Model	Dataset	Format	Resolution	Quantization	Board	Execution Engine	Frequency	Inference time (ms)	%NPU	%GPU	%CPU	X-LINUX-AI version	Framework
YOLOv8n-seg	person_coco_2017	Int8	256x256x3	per-channel**	STM32MP257F-EV1	NPU/GPU	800 MHz	19.84	91.71	8.29	0	v6.1.0	TensorFlow Lite
YOLOv8n-seg	person_coco_2017	Int8	320x320x3	per-channel**	STM32MP257F-EV1	NPU/GPU	800 MHz	30.97	93.59	6.41	0	v6.1.0	TensorFlow Lite

** To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization

** Note: On STM32MP2 devices, per-channel quantized models are internally converted to per-tensor quantization by the compiler using an entropy-based method. This may introduce a slight loss in accuracy compared to the original per-channel models.

Retraining and Integration in a Simple Example

Please refer to the stm32ai-modelzoo-services GitHub here. For instance segmentation, the models are stored in the Ultralytics repository. You can find them at the following link: Ultralytics YOLOv8-STEdgeAI Models.

Please refer to the Ultralytics documentation to retrain the model.

References

[1] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, "Microsoft COCO: Common Objects in Context." European Conference on Computer Vision (ECCV), 2014. Link

[2] Ultralytics, "YOLOv8: Next-Generation Object Detection and Segmentation Model." Ultralytics, 2023. Link