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--- |
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license: other |
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license_name: sla0044 |
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license_link: >- |
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https://github.com/STMicroelectronics/stm32aimodelzoo/pose_estimation/yolov8n_pose/LICENSE.md |
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pipeline_tag: keypoint-detection |
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--- |
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# Yolov8n_pose quantized |
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## **Use case** : `Pose estimation` |
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# Model description |
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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. |
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Yolov8n_pose is implemented in Pytorch by Ultralytics and is quantized in int8 format using tensorflow lite converter. |
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## Network information |
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| Network information | Value | |
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|-------------------------|-----------------| |
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| Framework | TensorFlow Lite | |
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| Quantization | int8 | |
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| Provenance | https://docs.ultralytics.com/tasks/pose/ | |
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## Networks inputs / outputs |
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With an image resolution of NxM with K keypoints to detect : |
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| Input Shape | Description | |
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| ----- | ----------- | |
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| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 | |
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| Output Shape | Description | |
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| ----- | ----------- | |
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| (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| |
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## Recommended Platforms |
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| Platform | Supported | Recommended | |
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|----------|-----------|-------------| |
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| STM32L0 | [] | [] | |
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| STM32L4 | [] | [] | |
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| STM32U5 | [] | [] | |
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| STM32H7 | [] | [] | |
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| STM32MP1 | [] | [] | |
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| STM32MP2 | [x] | [x] | |
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| STM32N6 | [x] | [x] | |
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# Performances |
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## Metrics |
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Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option. |
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### Reference **NPU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset) |
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|Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB)| STM32Cube.AI version | STEdgeAI Core version | |
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|----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------| |
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| [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 | |
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| [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 | |
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| [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 | |
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### Reference **NPU** inference time based on COCO Person dataset (see Accuracy for details on dataset) |
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| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version | |
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|--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------| |
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| [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 | |
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| [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 | |
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| [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 | |
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### Reference **MPU** inference time based on COCO Person dataset (see Accuracy for details on dataset) |
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Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework | |
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|-----------|--------|------------|---------------|-------------------|------------------|-----------|---------------------|-------|-------|------|--------------------|-----------------------| |
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| [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 | |
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| [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 | |
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** **To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization** |
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### AP0.5 on COCO Person dataset |
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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 |
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| Model | Format | Resolution | AP0.5* | |
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|-------|--------|------------|----------------| |
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| [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 % | |
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| [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 % | |
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| [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 % | |
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| [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 % | |
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\* NMS_THRESH = 0.1, SCORE_THRESH = 0.001 |
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## Integration in a simple example and other services support: |
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Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services). |
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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/). |
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Please refer to the [Ultralytics documentation](https://docs.ultralytics.com/tasks/pose/#train) to retrain the models. |
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# References |
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<a id="1">[1]</a> |
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“Microsoft COCO: Common Objects in Context”. [Online]. Available: https://cocodataset.org/#download. |
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@article{DBLP:journals/corr/LinMBHPRDZ14, |
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author = {Tsung{-}Yi Lin and |
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Michael Maire and |
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Serge J. Belongie and |
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Lubomir D. Bourdev and |
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Ross B. Girshick and |
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James Hays and |
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Pietro Perona and |
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Deva Ramanan and |
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Piotr Doll{'{a} }r and |
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C. Lawrence Zitnick}, |
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title = {Microsoft {COCO:} Common Objects in Context}, |
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journal = {CoRR}, |
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volume = {abs/1405.0312}, |
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year = {2014}, |
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url = {http://arxiv.org/abs/1405.0312}, |
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archivePrefix = {arXiv}, |
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eprint = {1405.0312}, |
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timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, |
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biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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