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Table 9 RetinaNet (focal loss) on COCO [88]

From: Survey on deep learning with class imbalance

 

Backbone

AP

\(AP_{50}\)

\(AP_{75}\)

\(AP_{S}\)

\(AP_{M}\)

\(AP_{L}\)

Two-stage methods

 Faster R-CNN+++

ResNet-101-C4

34.9

55.7

37.4

15.6

38.7

50.9

 Faster R-CNN w FPN

ResNet-101-FPN

36.2

59.1

39.0

18.2

39.0

48.2

 Faster R-CNN by G-RMI

Inception-ResNet-v2

34.7

55.5

36.7

13.5

38.1

52.0

 Faster R-CNN w TDM

Inception-ResNet-v2-TDM

36.8

57.7

39.2

16.2

39.8

52.1

One-stage methods

 YOLOv2

DarkNet-19

21.6

44.0

19.2

5.0

22.4

35.5

 SSD513

ResNet-101-SSD

31.2

50.4

33.3

10.2

34.5

49.8

 DSSD513

ResNet-101-DSSD

33.2

53.3

35.2

13.0

35.4

51.1

 RetinaNet

ResNet-101-FPN

39.1

59.1

42.3

21.8

42.7

50.2

 RetinaNet

ResNeXt-101-FPN

40.8

61.1

44.1

24.1

44.2

51.2

  1. Italic scores indicate top AP performances