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Table 6 Chalkiness Segmentation: results of the weakly supervised Grad-CAM approach with the best performing classification models as backbone

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

Model GT-known Loc. Acc. (%) Loc. Acc. (%) Avg. IoU (%) Layer T (%)
Grad-CAM (DenseNet-121) 51.20 = 085/166 51.20 = 085/166 47.44 Features_
denseblock2_
denselayer7_
conv2
60
Grad-CAM (ResNet-101) 84.34 = 140/166 83.13 = 138/166 68.11 Layer2_0_
conv2
60
Grad-CAM (SqueezeNet-1.0) 15.06 = 025/166 0 = 00/166 31.01 Features_12
_expand1x1
60
Grad-CAM (VGG-16) 7.23 = 012/166 7.23 = 012/166 24.92 Features_
module_5
60
Grad-CAM (EfficientNetB4) 28.92 = 048/166 28.92 = 048/166 35.40 Stem
_conv
50
Mask R-CNN (ResNet-101) 18.67 = 031/166 N/A 29.63 N/A N/A
  1. The results of Mask R-CNN with ResNet-101 as backbone are also shown. Only the 166 chalky seed images in the test set were used for chalkiness segmentation evaluation. Performance is reported using the following metrics (as applicable): Ground-Truth Localization Accuracy (GT-known Loc. Acc.), which represents the fraction of ground-truth chalky seed images with \(\text{ IoU } \ge 0.5\); Localization Accuracy (Loc. Acc.), which represents the fraction of ground-truth chalky images, with \(\text{ IoU } \ge 0.5\), correctly predicted by the model; Average IoU (Avg. IoU), which represents the average IoU for the set of chalky seed images. To calculate the IoU, the mask of the predicted chalkiness is obtained using a threshold \(T=60\%\) of the maximum pixel intensity. The last two columns show the layer that was used for generating the heatmap and the threshold used to binarize the heatmap when calculating IoU, respectively