--- 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 [1] “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} }