Method

yolo4_5 [yolo4_5l]


Submitted on 9 Aug. 2020 06:59 by
Zhuohui Zhang (Hefei University of Technology)

Running time:0.02 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
add two yolo layers
Parameters:
lr
batch_size
Latex Bibtex:

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) 93.35 % 91.71 % 79.49 %
Car (Orientation) 37.92 % 37.14 % 32.31 %
Pedestrian (Detection) 73.14 % 56.46 % 49.57 %
Pedestrian (Orientation) 40.97 % 31.53 % 27.63 %
Cyclist (Detection) 75.21 % 55.42 % 48.57 %
Cyclist (Orientation) 31.36 % 23.96 % 21.02 %
This table as LaTeX


2D object detection results.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot




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