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README.md

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-license: other
-license_name: sla0044
-license_link: >-
-  https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/main/pose_estimation/LICENSE.md
----
+---
+license: other
+license_name: sla0044
+license_link: >-
+  https://github.com/STMicroelectronics/stm32ai-modelzoo/raw/refs/heads/main/pose_estimation/LICENSE.md
+pipeline_tag: keypoint-detection
+---
+# Yolov11n_pose quantized
+
+## **Use case** : `Pose estimation`
+
+# Model description
+
+Yolov11n_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 Yolov11n_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.
+
+Yolov11n_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 | []        | []         |
+| STM32N6  | [x]       | [x]         |
+
+
+# Performances
+
+## Metrics
+
+Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
+> [!CAUTION] 
+> All YOLOv11 hyperlinks in the tables below link to an external GitHub folder, which is subject to its own license terms:
+https://github.com/stm32-hotspot/ultralytics/blob/main/LICENSE
+Please also check the folder's README.md file for detailed information about its use and content:
+https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/README.md
+
+### 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/yolo11/yolo11n_256_quant_pc_uf_pose_coco-st.tflite)  | COCO-Person      | Int8     | 256x256x3  | STM32N6   |   742.95      |      0.0          |    3543.04         |       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 |
+|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
+| [YOLOv8n pose per channel](https://github.com/stm32-hotspot/ultralytics/blob/main/examples/YOLOv8-STEdgeAI/stedgeai_models/pose_estimation/yolo11/yolo11n_256_quant_pc_uf_pose_coco-st.tflite) | COCO-Person      | Int8     | 256x256x3  | STM32N6570-DK   |   NPU/MCU      |   37.39             |     26.74        |       10.2.0        |     2.2.0   |
+
+
+
+## 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}
+}