Method

Mix-Teaching [Mix-Teaching ]
https://github.com/yanglei18/Mix-Teaching

Submitted on 28 Dec. 2022 14:28 by
Yang Lei (Tsinghua University)

Running time:30 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose Mix-Teaching, an effective semi-
supervised learning framework applicable to employ
both labeled and unlabeled images in training stage.
Parameters:
\beta=0.5
Latex Bibtex:
@article{Yang2022MixTeachingAS,
title={Mix-Teaching: A Simple, Unified and
Effective Semi-Supervised Learning Framework for
Monocular 3D Object Detection},
author={Lei Yang and Xinyu Zhang and Li Wang and
Minghan Zhu and Chuan-Fang Zhang and Jun Li},
journal={ArXiv},
year={2022},
volume={abs/2207.04448}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 96.35 % 91.02 % 83.41 %
Car (Orientation) 96.31 % 90.84 % 83.11 %
Car (3D Detection) 26.89 % 18.54 % 15.79 %
Car (Bird's Eye View) 35.74 % 24.23 % 20.80 %
Pedestrian (Detection) 67.34 % 53.52 % 47.45 %
Pedestrian (Orientation) 64.04 % 50.19 % 44.37 %
Pedestrian (3D Detection) 11.67 % 7.47 % 6.61 %
Pedestrian (Bird's Eye View) 12.34 % 8.40 % 7.06 %
Cyclist (Detection) 75.15 % 58.65 % 50.54 %
Cyclist (Orientation) 70.90 % 54.00 % 46.66 %
Cyclist (3D Detection) 8.04 % 4.91 % 4.15 %
Cyclist (Bird's Eye View) 8.56 % 5.36 % 4.62 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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