You can deploy this model and start earning money today!

STMicroelectronics/
yolov11n

object-detection

1405.0312

other

Views

No views yet

Yolo11n object detection quantized

Use case : `Object detection`

Model description

Yolo11n is a lightweight and efficient object detection 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 Yolo11n 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.

Yolo11n 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/detect/

Networks inputs / outputs

With an image resolution of NxM and K classes to detect:

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

Output Shape	Description
(1, 4+K, F)	FLOAT values Where F = (N/8)^2 + (N/16)^2 + (N/32)^2 is the 3 concatenated feature maps

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	External RAM	Weights Flash	STEdgeAI Core version
YOLO11n per channel	COCO-Person	Int8	256x256x3	STM32N6	656	0	2535.83	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
YOLO11n per channel	COCO-Person	Int8	256x256x3	STM32N6570-DK	NPU/MCU	26.37	36.50	3.0.0

AP on COCO Person dataset

Dataset details: link , License CC BY 4.0 , Quotation[1] , Number of classes: 80, Number of images: 118,287

Model	Format	Resolution	AP*
YOLOv11n per channel	Int8	640x640x3	64 %

* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100

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