Packaged version of the Yolor repository
Project description
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}
}
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Source Distribution
yolor-0.0.6.tar.gz
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