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STMicroelectronics/
yolov11n_pose

keypoint-detection

1405.0312

other

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Yolov11n_pose quantized

Use case : `Pose estimation`

Model description

Yolov11n_pose is a lightweight and efficient model designed for multi pose estimation 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 Yolov11n_pose 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.

Yolov11n_pose is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter.

Network information

Network information	Value
Framework	TensorFlow Lite
Quantization	int8
Provenance	https://docs.ultralytics.com/tasks/pose/

Networks inputs / outputs

With an image resolution of NxM with K keypoints to detect :

Input Shape	Description
(1, N, M, 3)	Single NxM RGB image with UINT8 values between 0 and 255

Output Shape	Description
(1, Kx3, F)	FLOAT values Where F = (N/8)^2 + (N/16)^2 + (N/32)^2 is the 3 concatenated feature maps and K is the number of keypoints

Recommended Platforms

Platform	Supported	Recommended
STM32L0	[]	[]
STM32L4	[]	[]
STM32U5	[]	[]
STM32H7	[]	[]
STM32MP1	[]	[]
STM32MP2	[]	[]
STM32N6	[x]	[x]

Performances

Metrics

Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.

[!CAUTION] All YOLOv11 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 Person dataset (see Accuracy for details on dataset)

Model	Dataset	Format	Resolution	Series	Internal RAM (KiB)	External RAM (KiB)	Weights Flash (KiB)	STEdgeAI Core version
YOLOv11n pose per channel	COCO-Person	Int8	256x256x3	STM32N6	740.25	0.0	2818.16	3.0.0
YOLOv11n pose per channel	COCO-Person	Int8	320x320x3	STM32N6	1840.85	0.0	2851.33	3.0.0

Reference NPU inference time based on COCO Person dataset (see Accuracy for details on dataset)

Model	Dataset	Format	Resolution	Board	Execution Engine	Inference time (ms)	Inf / sec	STEdgeAI Core version
YOLOv11n pose per channel	COCO-Person	Int8	256x256x3	STM32N6570-DK	NPU/MCU	34.32	29.14	3.0.0
YOLOv11n pose per channel	COCO-Person	Int8	320x320x3	STM32N6570-DK	NPU/MCU	56.15	17.81	3.0.0

Integration in a simple example and other services support:

Please refer to the stm32ai-modelzoo-services GitHub here. 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 models.

References

[1] “Microsoft COCO: Common Objects in Context”. [Online]. Available: https://cocodataset.org/#download. @article{DBLP:journals/corr/LinMBHPRDZ14, author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{'{a} }r and C. Lawrence Zitnick}, title = {Microsoft {COCO:} Common Objects in Context}, journal = {CoRR}, volume = {abs/1405.0312}, year = {2014}, url = {http://arxiv.org/abs/1405.0312}, archivePrefix = {arXiv}, eprint = {1405.0312}, timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14}, bibsource = {dblp computer science bibliography, https://dblp.org} }

STMicroelectronics/yolov11n_pose