MoveNet quantized

April 16, 2026 · View on GitHub

Model description

MoveNet is a single pose estimation model targeted for real-time processing implemented in Tensorflow.

The model is quantized in int8 format using tensorflow lite converter.

Network information

Network information	Value
Framework	TensorFlow Lite
Quantization	int8
Provenance	https://www.kaggle.com/models/google/movenet
Paper	https://storage.googleapis.com/movenet/MoveNet.SinglePose%20Model%20Card.pdf

Networks inputs / outputs

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

For heatmaps models

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

Output Shape	Description
(1, W, H, K)	FLOAT values Where WXH is the resolution of the output heatmaps and K is the number of keypoints

For the other models

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

Output Shape	Description
(1, Kx3)	FLOAT values Where Kx3 are the (x,y,conf) values of each keypoints

Recommended Platforms

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

Performances

Metrics

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

Reference NPU memory footprint based on COCO Person dataset (see Accuracy for details on dataset)

Model	Dataset	Format	Resolution	Series	Internal RAM (KiB)	Weights Flash (KiB)	STEdgeAI Core version
ST MoveNet Lightning heatmaps	COCO-Person	Int8	192x192x3	STM32N6	914.88	2304.0	4.0.0
ST MoveNet Lightning heatmaps	COCO-Person	Int8	224x224x3	STM32N6	1239.04	2304.0	4.0.0
ST MoveNet Lightning heatmaps	COCO-Person	Int8	256x256x3	STM32N6	1607.68	2304.0	4.0.0

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
ST MoveNet Lightning heatmaps	COCO-Person	Int8	192x192x3	STM32N6570-DK	NPU/MCU	22.17	45.1	4.0.0
ST MoveNet Lightning heatmaps	COCO-Person	Int8	224x224x3	STM32N6570-DK	NPU/MCU	27.0	37.0	4.0.0
ST MoveNet Lightning heatmaps	COCO-Person	Int8	256x256x3	STM32N6570-DK	NPU/MCU	34.16	29.2	4.0.0

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

Model	Dataset	Format	Resolution	Quantization	Board	Execution Engine	Frequency	Inference time (ms)	%NPU	%GPU	%CPU	X-LINUX-AI version	Framework
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	192x192x3	per-channel**	STM32MP257F-EV1	NPU/GPU	800 MHz	55.81	2.87	97.13	0	v6.1.0	OpenVX
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	224x224x3	per-channel**	STM32MP257F-EV1	NPU/GPU	800 MHz	79.41	2.41	97.59	0	v6.1.0	OpenVX
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	256x256x3	per-channel**	STM32MP257F-EV1	NPU/GPU	800 MHz	68.42	3.32	96.68	0	v6.1.0	OpenVX
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	192x192x3	per-tensor	STM32MP257F-EV1	NPU/GPU	800 MHz	8.20	82.06	17.94	0	v6.1.0	OpenVX
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	224x224x3	per-tensor	STM32MP257F-EV1	NPU/GPU	800 MHz	11.63	83.75	16.25	0	v6.1.0	OpenVX
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	256x256x3	per-tensor	STM32MP257F-EV1	NPU/GPU	800 MHz	13.10	81.39	18.61	0	v6.1.0	OpenVX
MoveNet Lightning	custom_dataset_person_17kpts	Int8	192x192x3	per-channel**	STM32MP257F-EV1	NPU/GPU	800 MHz	63.80	6.58	93.42	0	v6.1.0	OpenVX
MoveNet Thunder	custom_dataset_person_17kpts	Int8	256x256x3	per-channel**	STM32MP257F-EV1	NPU/GPU	800 MHz	183.49	3.47	96.53	0	v6.1.0	OpenVX
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	192x192x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	315.44	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	224x224x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	416.98	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	256x256x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	533.61	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	192x192x3	per-tensor	STM32MP157F-DK2	2 CPU	800 MHz	424.77	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	224x224x3	per-tensor	STM32MP157F-DK2	2 CPU	800 MHz	558.26	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	256x256x3	per-tensor	STM32MP157F-DK2	2 CPU	800 MHz	727.03	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
MoveNet Lightning	custom_dataset_person_17kpts	Int8	192x192x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	196.81	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
MoveNet Thunder	custom_dataset_person_17kpts	Int8	256x256x3	per-channel	STM32MP157F-DK2	2 CPU	800 MHz	766.38	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	192x192x3	per-channel	STM32MP135F-DK	1 CPU	1000 MHz	484.64	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	224x224x3	per-channel	STM32MP135F-DK	1 CPU	1000 MHz	651.62	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps	custom_coco_person_17kpts	Int8	256x256x3	per-channel	STM32MP135F-DK	1 CPU	1000 MHz	844.89	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	192x192x3	per-tensor	STM32MP135F-DK	1 CPU	1000 MHz	578.72	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	224x224x3	per-tensor	STM32MP135F-DK	1 CPU	1000 MHz	772.76	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
ST MoveNet Lightning heatmaps per-tensor	custom_coco_person_17kpts	Int8	256x256x3	per-tensor	STM32MP135F-DK	1 CPU	1000 MHz	1007.57	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
MoveNet Lightning	custom_dataset_person_17kpts	Int8	192x192x3	per-channel	STM32MP135F-DK	1 CPU	1000 MHz	306.34	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0
MoveNet Thunder	custom_dataset_person_17kpts	Int8	256x256x3	per-channel	STM32MP135F-DK	1 CPU	1000 MHz	1131.30	NA	NA	100	v6.1.0	TensorFlowLite 2.18.0

** 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.

OKS 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	Training Dataset	OKS
ST MoveNet Lightning heatmaps per-channel	Int8	192x192x3	custom ST	57.64 %
ST MoveNet Lightning heatmaps per-channel	Int8	224x224x3	custom ST	62.29 %
ST MoveNet Lightning heatmaps per-channel	Int8	256x256x3	custom ST	62.36 %
ST MoveNet Lightning heatmaps per-tensor	Int8	192x192x3	custom ST	55.84 %
ST MoveNet Lightning heatmaps per-tensor	Int8	224x224x3	custom ST	58.95 %
ST MoveNet Lightning heatmaps per-tensor	Int8	256x256x3	custom ST	60.73 %
ST MoveNet Lightning heatmaps per-channel	Int8	192x192x3	COCO	55.34 %
ST MoveNet Lightning heatmaps per-channel	Int8	224x224x3	COCO	59.02 %
ST MoveNet Lightning heatmaps per-channel	Int8	256x256x3	COCO	61.99 %
ST MoveNet Lightning heatmaps per-tensor	Int8	192x192x3	COCO	55.34 %
ST MoveNet Lightning heatmaps per-tensor	Int8	224x224x3	COCO	58.50 %
ST MoveNet Lightning heatmaps per-tensor	Int8	256x256x3	COCO	61.63 %
MoveNet Lightning per-channel	Int8	192x192x3	custom Google	54.12 %
MoveNet Thunder per-channel	Int8	256x256x3	custom Google	64.43 %

Integration in a simple example and other services support:

Please refer to the stm32ai-modelzoo-services GitHub here

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} }