Method

YOLOStereo3D with shufflenet-v2 backbone knowledge distillation [BKDStereo3D]
[Anonymous Submission]

Submitted on 8 Aug. 2023 11:46 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
changing YOLOStereo3D's Resnet 34 backbone to
shufflenet v2 backbone, Knowledge distillation
using L2 norm with projector structure.
Parameters:
learning rate : 0.001
another is same with YOLOStereo3D
Latex Bibtex:
inproceedings

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) 94.61 % 84.10 % 61.85 %
Car (Orientation) 93.50 % 82.12 % 60.34 %
Car (3D Detection) 59.38 % 35.23 % 25.24 %
Car (Bird's Eye View) 70.19 % 44.02 % 32.78 %
Pedestrian (Detection) 55.94 % 41.17 % 34.99 %
Pedestrian (Orientation) 38.65 % 27.64 % 23.62 %
Pedestrian (3D Detection) 23.48 % 15.76 % 13.73 %
Pedestrian (Bird's Eye View) 25.47 % 17.44 % 14.44 %
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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