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Table 3 Classification results on polished rice with various networks as backbone in the weakly supervised Grad-CAM approach

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

Model Acc.(%) Chalky Non-chalky
Pre.(%) Rec.(%) F1(%) Pre.(%) Rec.(%) F1(%)
DenseNet-121 95.61 94.58 94.58 94.58 96.31 96.31 96.31
DenseNet-161 95.12 92.44 95.78 94.08 97.06 94.67 95.85
DenseNet-169 94.63 92.86 93.98 93.41 95.87 95.08 95.47
ResNet-18 94.63 94.44 92.17 93.29 94.76 96.31 95.53
ResNet-34 94.15 93.29 92.17 92.73 94.72 95.49 95.10
ResNet-50 94.88 95.03 92.17 93.58 94.78 96.72 95.74
ResNet-101 95.12 93.45 94.58 94.01 96.28 95.49 95.88
ResNet-152 94.88 93.94 93.37 93.66 95.51 95.90 95.71
SqueezeNet-1.0 95.12 93.45 94.58 94.01 96.28 95.49 95.88
SqueezeNet-1.1 94.39 91.33 95.18 93.22 96.62 93.85 95.22
VGG-11 94.88 93.94 93.37 93.66 95.51 95.90 95.71
VGG-13 94.39 92.31 93.98 93.13 95.85 94.67 95.26
VGG-16 95.12 92.94 95.18 94.05 96.67 95.08 95.87
VGG-19 94.15 90.34 95.78 92.98 97.01 93.03 94.98
EfficientNetB0 95.13 93.98 93.98 93.98 95.92 95.92 95.92
EfficientNetB1 95.13 94.51 93.37 93.94 95.55 96.33 95.93
EfficientNetB2 93.67 90.23 94.58 92.35 96.20 93.06 94.61
EfficientNetB3 95.13 95.06 92.77 93.90 95.18 96.73 95.95
EfficientNetB4 95.38 96.82 91.57 94.12 94.49 97.96 96.19
EfficientNetB5 93.67 91.67 92.77 92.22 95.06 94.29 94.67
EfficientNetB6 94.16 92.77 92.77 92.77 95.10 95.10 95.10
  1. The number following a network’s name denotes the number of layers in the network (as in DenseNet-121 or ResNet-101) or the version of the network (as in SqueezeNet-1.0 or EfficientNetB0). Performance is reported in terms of Accuracy (Acc.), Precision (Pre.), Recall (Rec.) and F1 measure (F1). Precision, Recall and F1 measure values are reported separately for the Chalky and Non-Chalky classes. All models are trained/tuned/evaluated on the same training/development/test splits. The results reported are obtained on the test set. The best performance for each type of model for each metric is highlighted using bold font