sassoftware/yolov7-pose

Implementation of paper YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss. Tailored for DLModelZoo action.

Yolov7-pose

Overview

This python package is an implementation of "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors".

Pose estimation implimentation is based on YOLO-Pose.

This is a tailored version for use of the SAS Viya DLModelZoo action set.

Installation

To install YOLOv7-Pose, use the following command:

pip install sas-yolov7-pose

Contributing

We welcome your contributions! Please read CONTRIBUTING.md for details on how to submit contributions to this project.

License

This project is licensed under the GNU GENERAL PUBLIC LICENSE 3.0 License.

Additional Resources

• https://github.com/AlexeyAB/darknet
• https://github.com/WongKinYiu/yolor
• https://github.com/WongKinYiu/PyTorch_YOLOv4
• https://github.com/WongKinYiu/ScaledYOLOv4
• https://github.com/Megvii-BaseDetection/YOLOX
• https://github.com/ultralytics/yolov3
• https://github.com/ultralytics/yolov5
• https://github.com/DingXiaoH/RepVGG
• https://github.com/JUGGHM/OREPA_CVPR2022
• https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose
• https://github.com/WongKinYiu/yolov7/tree/pose