| | --- |
| | license: apache-2.0 |
| | license_link: >- |
| | https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/main/pose_estimation/LICENSE.md |
| | pipeline_tag: keypoint-detection |
| | --- |
| | # 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) | STEdgeAI Core version | |
| | |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|-------------------------| |
| | | [handlandmarks](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/pose_estimation/handlandmarks/custom_dataset_hands_21kpts/handlandmarks_full_224_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/pose_estimation/handlandmarks/custom_dataset_hands_21kpts/handlandmarks_full_224_int8.tflite) | custom_dataset_hands_21kpts | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 20.75 | 48.19 | 3.0.0 | |
| | |
| | |
