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
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) | STEdgeAI Core version |
|---|
| handlandmarks | custom_dataset_hands_21kpts | Int8 | 224x224x3 | STM32N6 | 1739.5 | 0.0 | 3283.38 | 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 |
|---|
| handlandmarks | custom_dataset_hands_21kpts | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 20.75 | 48.19 | 3.0.0 |
