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Table 3 Selection of visual feature extraction network based on % accuracy scores

From: HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance

Dataset\(\rightarrow \) Y1/Y1 Y2/Y2 Whole dataset
Network \(\downarrow \) IA FIA LA IA FIA LA IA FIA LA
ResNet18 94.55 94.87 98.71 84.00 82.00 90.50 83.19 78.41 89.56
ResNet34 94.09 94.87 97.43 84.37 83.50 90.00 84.85 81.65 91.36
ResNet50 94.43 96.15 98.71 82.43 77.50 88.00 82.60 79.85 89.92
ResNet101 93.04 91.02 98.71 82.75 83.00 88.50 82.49 83.09 88.12
ResNet152 93.27 91.02 97.43 80.81 74.50 86.00 82.60 79.85 87.76
  1. For each network structure tested, % accuracy scores are reported as Image Accuracy (IA), First Image Accuracy (FIA) and Leaf Accuracy (LA). The highest accuracy in each column and corresponding feature extractor is highlighted in bold. All models here employ the Adam optimizer with learning rate (lr) \(=1e^{-4}\)