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Table 2 Scores of models with E-loss based on modified architectures (except GapNet)

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

Model with E-loss

Macro F1

ResNet50 (fold 0)

0.770

ResNet50 (fold 1)

0.766

ResNet50 (fold 2)

0.768

ResNet50 (fold 3)

0.758

ResNet50 (fold 4)

0.762

InceptionV4 (single fold)

0.767

SENet (single fold)

0.782

Dense50 (random fold 1)

0.758

Dense50 (random fold 2)

0.761

Dense50 (random fold 3)

0.766

ResNet101 (single fold)

0.778

GapNet-PL

0.765

  1. The bold of “0.782” means that the modified SENet performs best in this stage
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BMC Bioinformatics

ISSN: 1471-2105

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