STMicroelectronics/hand_landmarks

Hand landmarks quantized

Use case : Pose estimation

Model description

Hand landmarks 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://github.com/PINTO0309/PINTO_model_zoo/tree/main/033_Hand_Detection_and_Tracking
Paper	https://storage.googleapis.com/mediapipe-assets/Model%20Card%20Hand%20Tracking%20(Lite_Full)%20with%20Fairness%20Oct%202021.pdf

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)	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)	External RAM (KiB)	Weights Flash (KiB)	STM32Cube.AI version	STEdgeAI Core version
hand_landmarks	COCO-Person	Int8	224x224x3	STM32N6	1739.5	0.0	3283.38	10.2.0	2.2.0

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	STM32Cube.AI version	STEdgeAI Core version
hand_landmarks	custom_dataset_hands_21kpts	Int8	224x224x3	STM32N6570-DK	NPU/MCU	20.75	48.19	10.2.0	2.2.0