Table 5 The detection results of the selected detectors on the proposed dataset.

From: A customized image editing framework for diverse prohibited and restricted products in illegal online transactions

Model

mAP (%)

Precision (%)

Recall (%)

F1-score (%)

FLOPS

Parameters

CenterNet

82.67

93.59

58.33

71.86

70.22G

32.67 MB

SSD

91.48

86.66

86.87

86.77

72.11G

38.85 MB

EfficientDet-D0

87.87

88.63

81.78

85.07

5.45G

3.90 MB

Faster R-CNN

89.82

67.65

93.29

78.43

372.52G

139.02 MB

RetinaNet

87.10

86.96

80.16

83.43

236.06G

57.69 MB

YOLOv3

87.77

88.91

82.04

85.34

66.43G

62.14 MB

YOLOv4

82.18

83.95

76.27

79.92

60.78G

64.55 MB

YOLOv5-X

88.17

88.10

84.92

86.48

220.25G

88.01 MB

YOLOv7

90.97

89.47

88.01

88.73

107.07G

37.81 MB

YOLOv7-tiny

81.82

87.16

72.31

79.05

14.16G

6.32 MB

YOLOv8-N

88.96

89.95

85.12

87.47

10.46G

3.50 MB

YOLOX-N

82.53

85.73

69.71

76.90

2.67G

0.92 MB