yolor

Yolor-Pip: Packaged version of the Yolor repository

Overview

This repo is a packaged version of the Yolor model.

Benchmark

Model	Test Size	APtest	AP50test	AP75test	batch1 throughput	batch32 inference
YOLOR-CSP	640	52.8%	71.2%	57.6%	106 fps	3.2 ms
YOLOR-CSP-X	640	54.8%	73.1%	59.7%	87 fps	5.5 ms
YOLOR-P6	1280	55.7%	73.3%	61.0%	76 fps	8.3 ms
YOLOR-W6	1280	56.9%	74.4%	62.2%	66 fps	10.7 ms
YOLOR-E6	1280	57.6%	75.2%	63.0%	45 fps	17.1 ms
YOLOR-D6	1280	58.2%	75.8%	63.8%	34 fps	21.8 ms
YOLOv4-P5	896	51.8%	70.3%	56.6%	41 fps (old)	-
YOLOv4-P6	1280	54.5%	72.6%	59.8%	30 fps (old)	-
YOLOv4-P7	1536	55.5%	73.4%	60.8%	16 fps (old)	-

Installation

pip install yolor

Yolov6 Inference

from yolor.helpers import Yolor

model = Yolor(cfg='yolor/cfg/yolor_p6.cfg', weights='yolor/yolor_p6.pt', imgsz=640, device='cuda:0')

model.classes = None
model.conf = 0.25
model.iou_ = 0.45
model.show = False
model.save = True

model.predict('yolor/data/highway.jpg')

Citation

@article{wang2021you,
  title={You Only Learn One Representation: Unified Network for Multiple Tasks},
  author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2105.04206},
  year={2021}
}