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Table 5 Variation of the Average IoU (%) with the layer and threshold used for ResNet-101

From: Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

Layer

T = 20%

T = 30%

T = 40%

T = 50%

T = 60%

T = 70%

T = 80%

layer1_2_conv2

0.20

9.90

18.41

26.08

37.53

18.55

18.55

layer2_0_conv2

3.81

19.86

31.53

44.90

68.11

18.55

18.55

layer3_1_conv2

1.77

9.59

18.92

28.22

41.59

18.55

18.55

layer4_1_conv3

0.15

10.26

15.43

21.10

29.68

18.55

18.55

  1. The layer is used to generate the heatmaps and the threshold T is used to binarize the heatmaps (e.g., \(T=20\%\) means that only pixels with values at least \(20\%\) of the max pixel value in the image are included in the binary mask). The layers were sampled to include a low-level layer (layer1_2_conv2), a high-level layer (layer4_1_conv3) and two intermediate layers (layer2_0_conv2 and layer3_1_conv2) that showed good results based on a qualitative inspection of the maps. The threshold T is varied from \(20\%\) to \(80\%\) in increments of 10. The best result and the corresponding layer and threshold are highlighted in bold font