Pose Estimation - Ultralytics YOLO11

This example shows how to run the Ultralytics YOLO11 pose estimation model on the BPU for human keypoint detection and visualization. It supports model preprocessing, inference, and post-processing (including keypoint decoding, bounding box drawing, and keypoint annotation). The sample code is located in /app/cdev_demo/bpu/04_pose_sample/01_ultralytics_yolo11_pose/.

This example shows how to run the Ultralytics YOLO11 pose estimation model on the BPU for human keypoint detection and visualization. It supports model preprocessing, inference, and post-processing (including keypoint decoding, bounding box drawing, and keypoint annotation). The sample code is located in /app/cdev_demo/bpu/pose_sample/ultralytics_yolo11_pose/.

Model Description

• Overview:

  Ultralytics YOLO11 Pose is an efficient lightweight human keypoint detection model that supports simultaneous object detection and pose estimation (multi-keypoint prediction). It integrates Distribution Focal Loss (DFL) to improve localization accuracy for both bounding boxes and keypoints, suitable for multi-person pose recognition in real-time applications.

• HBM model name: yolo11n_pose_nashe_640x640_nv12.hbm

• HBM model name: yolo11n_pose_nashp_640x640_nv12.hbm

• Input format: NV12 image (Y and UV separated), size 640×640

• Output:

  • Bounding box coordinates for each person (xyxy)

  • Keypoint positions (K×2, x/y coordinates)

  • Confidence score for each keypoint

  • Supports COCO human keypoint format (typically 17 keypoints)

Feature Overview

• Model loading

  Load the specified Ultralytics YOLO11 pose estimation model and automatically parse model metadata.

• Input preprocessing

  Resize the input BGR image to 640×640 and convert it to NV12 format (Y and UV separated) for model inference.

• Inference execution

  Run inference via the .infer() interface.

• Result post-processing

  • Decode bounding boxes from multi-scale outputs (using DFL bin decoding);

  • Decode keypoint positions and confidence scores (K×2 + K);

  • Apply NMS to remove redundant detection boxes;

  • Map keypoint coordinates and bounding boxes back to the original image size;

  • Use threshold controls to display only high-confidence keypoints;

  • Support image visualization including detection boxes and keypoint drawing.

Environment Dependencies

Before building and running, ensure the following dependencies are installed:

sudo apt update
sudo apt install libgflags-dev

Directory Structure

.
|-- CMakeLists.txt                     # CMake build script: targets, dependencies, include paths
|-- README.md                          # Usage instructions (this file)
|-- inc
|   `-- ultralytics_yolo11_pose.hpp    # Model wrapper header: load/preprocess/infer/post-process interface declarations
`-- src
    |-- main.cc                        # Program entry: parse arguments → full pipeline → save visualization
    `-- ultralytics_yolo11_pose.cc     # Model implementation: decoding, NMS, keypoint post-processing, coordinate restoration

Build the Project

• Configure and build

  mkdir build && cd build
  cmake ..
  make -j$(nproc)

Model Download

If the model is not found at runtime, download it with the following command:

wget https://archive.d-robotics.cc/downloads/rdk_model_zoo/rdk_s100/ultralytics_YOLO/yolo11n_pose_nashe_640x640_nv12.hbm

wget https://archive.d-robotics.cc/downloads/rdk_model_zoo/rdk_s600/ultralytics_YOLO/yolo11n_pose_nashp_640x640_nv12.hbm

Parameter Reference

Parameter	Description	Default Value
--model_path	Model file path (.hbm)	/opt/hobot/model/s100/basic/yolo11n_pose_nashe_640x640_nv12.hbm
--test_img	Input test image path	/app/res/assets/bus.jpg
--label_file	Class label file (one class name per line)	/app/res/labels/coco_classes.names
--score_thres	Confidence threshold (detections below this are filtered)	0.25
--nms_thres	IoU threshold (intra-class NMS deduplication)	0.7
--kpt_conf_thres	Keypoint visualization confidence threshold (points below this are not shown)	0.5

Parameter	Description	Default Value
--model_path	Model file path (.hbm)	/opt/hobot/model/s600/basic/yolo11n_pose_nashp_640x640_nv12.hbm
--test_img	Input test image path	/app/res/assets/bus.jpg
--label_file	Class label file (one class name per line)	/app/res/labels/coco_classes.names
--score_thres	Confidence threshold (detections below this are filtered)	0.25
--nms_thres	IoU threshold (intra-class NMS deduplication)	0.7
--kpt_conf_thres	Keypoint visualization confidence threshold (points below this are not shown)	0.5

Quick Start

• Run the model

  • Make sure you are in the build directory

  • Use default parameters

    ./ultralytics_yolo11_pose

  • Run with custom parameters

    ./ultralytics_yolo11_pose \
    --model_path /opt/hobot/model/s100/basic/yolo11n_pose_nashe_640x640_nv12.hbm \
    --test_img   /app/res/assets/bus.jpg \
    --label_file /app/res/labels/coco_classes.names \
    --score_thres 0.25 \
    --nms_thres   0.7 \
    --kpt_conf_thres 0.5

    ./ultralytics_yolo11_pose \
    --model_path /opt/hobot/model/s600/basic/yolo11n_pose_nashp_640x640_nv12.hbm \
    --test_img   /app/res/assets/bus.jpg \
    --label_file /app/res/labels/coco_classes.names \
    --score_thres 0.25 \
    --nms_thres   0.7 \
    --kpt_conf_thres 0.5

• View the results

  After a successful run, the result is drawn on the original image and saved to build/result.jpg.

  [Saved] Result saved to: result.jpg

Notes

• The output is saved as result.jpg for you to inspect.

• For more deployment options or model support information, refer to the official documentation or contact platform technical support.

License

Copyright (C) 2025，XiangshunZhao D-Robotics.

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.