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Yolor-Pip: Packaged version of the Yolor repository

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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}
}

Metadata

Release files for yolor 0.0.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for yolor 0.0.6
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Release files / yolor-0.0.6.tar.gz

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Uploaded via twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.2.0 requests-toolbelt/0.8.0 tqdm/4.30.0 CPython/3.8.10

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