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Table 3 Comparisons of detection performance and inference speed for YOLOV4-tiny by using the proposed and baseline approach on model development test set data

From: Tile-based microscopic image processing for malaria screening using a deep learning approach

Model

Tile size

AP (%)

R (%)

Time (sec/img)

Train

Inference

YOLOV4-tiny@416

1088 \(\times\) 1088

1088 \(\times\) 1088

56.0

60.0

1.0

832 \(\times\) 832

67.0

73.0

1.0

608 \(\times\) 608

78.0

86.0

1.4

416 \(\times\) 416

65.0

75.8

1.4

YOLOV4-tiny@512

1088 \(\times\) 1088

1088 \(\times\) 1088

70.0

75.9

1.0

832 \(\times\) 832

80.4

86.1

1.2

608 \(\times\) 608

82.6

91.4

1.6

YOLOV4-tiny@608

1088 \(\times\) 1088

1088 \(\times\) 1088

77.9

83.4

1.0

832 \(\times\) 832

85.9

92.7

1.3

608 \(\times\) 608

81.9

89.3

1.7

YOLOV4-tiny@416

832 \(\times\) 832

1088 \(\times\) 1088

53.3

57.6

1.0

832 \(\times\) 832

66.0

71.9

1.1

608 \(\times\) 608

81.4

88.1

1.6

YOLOV4-tiny@512

832 \(\times\) 832

1088 \(\times\) 1088

66.7

72.8

1.0

832 \(\times\) 832

79.1

85.0

1.1

608 \(\times\) 608

86.0

94.1

1.6

416 \(\times\) 416

78.8

89.2

2.7

YOLOV4-tiny@608

832 \(\times\) 832

1088 \(\times\) 1088

76.7

82.6

1.0

832 \(\times\) 832

86.0

92.9

1.3

608 \(\times\) 608

84.4

91.5

1.6

YOLOV4-tiny@416

608 \(\times\) 608

1088 \(\times\) 1088

48.0

57.1

1.0

832 \(\times\) 832

60.9

67.1

1.0

608 \(\times\) 608

83.2

90.1

1.3

416 \(\times\) 416

84.0

91.1

2.0

YOLOV4-tiny@512

608 \(\times\) 608

1088 \(\times\) 1088

54.3

60.8

1.0

832 \(\times\) 832

76.4

79.8

1.0

608 \(\times\) 608

87.1

95.3

1.5

416 \(\times\) 416

84.0

94.9

2.6

YOLOV4-tiny@608

608 \(\times\) 608

1088 \(\times\) 1088

69.4

85.7

1.0

832 \(\times\) 832

84.3

92.1

1.3

608 \(\times\) 608

87.0

95.3

1.9

Without tiling

YoloV4-tiny@416

–

–

54.0

21.0

0.15

YoloV4-tiny@512

–

–

69.0

48.0

0.15

YoloV4-tiny@608

–

–

76.0

57.0

0.15

  1. Bold values indicate the best-performing model