Implementation of "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors" combined with "Whole-Body Human Pose Estimation in the Wild".
This repo seeks to combine the aforementioned papers/repos to add extra keypoints to yolo-pose models.
Pose estimation implimentation is based on YOLO-Pose.
python train.py --data data/coco_kpts.yaml --cfg cfg/yolov7-tiny-pose.yaml --batch-size 64 --img 640 --kpt-label --sync-bn --device 0 --hyp data/hyp.pose.yaml --nkpt 133 --weights PATH_TO_PRETRAINED_WEIGHTS epochs 500[Keypoints Labels of MS COCO 2017]
COCO Whole-Body: https://github.com/jin-s13/COCO-WholeBody
Handy COCO to YOLO conversion script in utils/coco2yolo.py.
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train.py --data data/coco_kpts.yaml --cfg cfg/yolov7-w6-pose.yaml --weights weights/yolov7-w6-person.pt --batch-size 128 --img 960 --kpt-label --sync-bn --device 0,1,2,3,4,5,6,7 --name yolov7-w6-pose --hyp data/hyp.pose.yamlTensorRT:https://github.com/nanmi/yolov7-pose
python test.py --data data/coco_kpts.yaml --img 960 --conf 0.001 --iou 0.65 --weights yolov7-w6-pose.pt --kpt-label@article{wang2022yolov7,
title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
journal={arXiv preprint arXiv:2207.02696},
year={2022}
}
Expand
- 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/jin-s13/COCO-WholeBody
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