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yolov8n_pose

Keypoint Detection
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yolov8n_pose
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---
license: other
license_name: sla0044
license_link: >-
  https://github.com/STMicroelectronics/stm32aimodelzoo/pose_estimation/yolov8n_pose/LICENSE.md
pipeline_tag: keypoint-detection
---
# Yolov8n_pose quantized

## **Use case** : `Pose estimation`

# Model description

Yolov8n_pose is a lightweight and efficient model designed for multi pose estimation tasks. It is part of the YOLO (You Only Look Once) family of models, known for their real-time object detection capabilities. The "n" in Yolov8n_pose indicates that it is a nano version, optimized for speed and resource efficiency, making it suitable for deployment on devices with limited computational power, such as mobile devices and embedded systems.

Yolov8n_pose is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter.

## Network information


| Network information     |  Value          |
|-------------------------|-----------------|
|  Framework              | TensorFlow Lite |
|  Quantization           | int8            |
|  Provenance             | https://docs.ultralytics.com/tasks/pose/ |


## 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, F) | FLOAT values Where F = (N/8)^2 + (N/16)^2 + (N/32)^2 is the 3 concatenated feature maps and K is the number of keypoints|


## Recommended Platforms


| Platform | Supported | Recommended |
|----------|-----------|-------------|
| STM32L0  | []        | []          |
| STM32L4  | []        | []          |
| STM32U5  | []        | []          |
| STM32H7  | []        | []          |
| STM32MP1 | []        | []          |
| 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 |
|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_192_quant_pc_uf_pose_coco-st.tflite)  | COCO-Person      | Int8     | 192x192x3  | STM32N6   |  477.56       |      0.0          |    3247.89         |       10.0.0        |     2.0.0   |
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_256_quant_pc_uf_pose_coco-st.tflite)  | COCO-Person      | Int8     | 256x256x3  | STM32N6   |   1135      |      0.0          |    3265.22         |       10.0.0        |     2.0.0   |
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_320_quant_pc_uf_pose_coco-st.tflite)  | COCO-Person      | Int8     | 320x320x3  | STM32N6   |   2264.27      |      0.0         |     3263.72        |       10.0.0        |     2.0.0   |

### 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   | STM32Cube.AI version  |  STEdgeAI Core version |
|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_192_quant_pc_uf_pose_coco-st.tflite) | COCO-Person      | Int8     | 192x192x3  | STM32N6570-DK   |   NPU/MCU      |    24.46            |     40.89        |       10.0.0        |     2.0.0   |
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_256_quant_pc_uf_pose_coco-st.tflite) | COCO-Person      | Int8     | 256x256x3  | STM32N6570-DK   |   NPU/MCU      |   35.79             |     27.95        |       10.0.0        |     2.0.0   |
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_320_quant_pc_uf_pose_coco-st.tflite) | COCO-Person      | Int8     | 320x320x3  | STM32N6570-DK   |   NPU/MCU      |   51.35             |      19.48       |       10.0.0        |     2.0.0   |


### Reference **MPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
 Model         | Format | Resolution | Quantization  | Board             | Execution Engine | Frequency | Inference time (ms) | %NPU  | %GPU  | %CPU | X-LINUX-AI version |       Framework       |
|-----------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------|
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_256_quant_pc_uf_pose_coco-st.tflite) | Int8   | 256x256x3  |  per-channel**  | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 102.8 ms            | 11.70  | 88.30 |0     | v5.0.0             | OpenVX                |
| [YOLOv8n pose per tensor](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_256_quant_pt_uf_pose_coco-st.tflite)  | Int8   | 256x256x3  |  per-tensor     | STM32MP257F-DK2   | NPU/GPU          | 800  MHz  | 17.57 ms            | 86.79  | 13.21 |0     | v5.0.0             | OpenVX                |

** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization**

### AP0.5 on COCO Person dataset


Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) , Quotation[[1]](#1) , Number of classes: 80, Number of images: 118,287


| Model | Format | Resolution |       AP0.5*   |
|-------|--------|------------|----------------|
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_192_quant_pc_uf_pose_coco-st.tflite) | Int8   | 192x192x3  | 41.05 %  |
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_256_quant_pc_uf_pose_coco-st.tflite) | Int8   | 256x256x3  | 51.12 %  |
| [YOLOv8n pose per tensor](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_256_quant_pt_uf_pose_coco-st.tflite)  | Int8   | 256x256x3  | 48.43 %  |
| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolov8n_320_quant_pc_uf_pose_coco-st.tflite) | Int8   | 320x320x3  | 55.55 %  |

\* NMS_THRESH = 0.1, SCORE_THRESH = 0.001

## Integration in a simple example and other services support:

Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services).
The models are stored in the Ultralytics repository. You can find them at the following link: [Ultralytics YOLOv8-STEdgeAI Models](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/).

Please refer to the [Ultralytics documentation](https://docs.ultralytics.com/tasks/pose/#train) to retrain the models.


# References

<a id="1">[1]</a>
“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}
}