Yunet
Use case : Face detection
Model description
Yunet is a lightweight and efficient face detection model optimized for real-time applications on embedded devices. Yunet designed specifically for detecting faces and 5 keypoints (2x eyes, 2x mouth, nose). The models are quantized to int8 format using ONNX QDQ to reduce memory footprint and improve inference speed on resource-constrained hardware.
Yunet is known for its fast inference and accuracy, making it suitable for applications such as face tracking, augmented reality, and user authentication.
Network information
Network inputs / outputs
| Input Shape | Description |
|---|
| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 |
YuNet produces multi-scale outputs for face detection and landmark localization. Yunet has 3 strides (32,16,8), for each stride S, outputs have the following shapes.
| Output Shape | Description |
|---|
| (1, F, 1) | Classification scores: Probability of face |
| (1, F, 1) | IoU scores: Predicted IoU |
| (1, F, 4) | Bounding box regression: [dx, dy, dw, dh] offsets |
| (1, F, 10) | Landmark regression: 5 facial landmarks (x, y) |
Where:
- F = (N/S)×(M/S) (Total number of detections for a given stride S)
Recommended Platforms
| Platform | Supported | Recommended |
|---|
| STM32L0 | [] | [] |
| STM32L4 | [] | [] |
| STM32U5 | [] | [] |
| STM32H7 | [] | [] |
| STM32MP1 | [] | [] |
| STM32MP2 | [] | [] |
| STM32N6 | [x] | [x] |
Performances
Metrics
Performance metrics are measured using default STM32Cube.AI configurations with input/output allocated buffers.
| Model | Dataset | Format | Resolution | Series | Internal RAM (KB) | External RAM (KB) | Weights Flash (KB) | STEdgeAI Core version |
|---|
| yunet 320x320 | WIDER FACE | Int8 | 3x320x320 | STM32N6 | 1130.49 | 0 | 92.31 | 3.0.0 |
Reference NPU inference time (example)
| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version |
|---|
| yunet 320x320 | WIDER FACE | Int8 | 3x320x320 | STM32N6570-DK | NPU/MCU | 6.74 | 147.36 | 3.0.0 |
Integration and support
For integration examples and additional services, please refer to the STM32 AI model zoo services repository:
https://github.com/STMicroelectronics/stm32ai-modelzoo-servicesReferences
