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Table 3 F1 of models with the proposed optimization strategies based on Table 2 (except GapNet)

From: Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks

Model TA TE TA + TE
ResNet50 (fold 0) 0.774 0.772 0.774
ResNet50 (fold 1) 0.769 0.771 0.773
ResNet50 (fold 2) 0.773 0.769 0.775
ResNet50 (fold 3) 0.763 0.760 0.765
ResNet50 (fold 4) 0.765 0.763 0.767
InceptionV4 (single fold) 0.772 0.770 0.772
SENet (single fold) 0.786 0.783 0.789
Dense50 (random fold 1) 0.762 0.760 0.765
Dense50 (random fold 2) 0.765 0.763 0.766
Dense50 (random fold 3) 0.771 0.769 0.774
ResNet101 (single fold) 0.782 0.784 0.785
GapNet-PL 0.765 0.765 0.765
  1. The bolds of “0.786, 0.784 and 0.789” mean that SENet, ResNet101 and SENet can reach up to the best macro F1 score for TA, TE and TA+TE, respectively