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Table 1 Comparison of the model performance (F1) between original and modified ones

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

Model Original Customized With hard sampler
ResNet50 (fold 0) 0.737 0.750 0.761
ResNet50 (fold 1) 0.729 0.734 0.752
ResNet50 (fold 2) 0.735 0.746 0.758
ResNet50 (fold 3) 0.731 0.741 0.749
ResNet50 (fold 4) 0.726 0.730 0.755
InceptionV4 (single fold) 0.736 0.749 0.756
SENet (single fold) 0.745 0.736 0.771
Dense50 (random fold 1) 0.732 0.738 0.741
Dense50 (random fold 2) 0.729 0.735 0.748
Dense50 (random fold 3) 0.732 0.741 0.754
ResNet101 (single fold) 0.742 0.758 0.760
  1. The bold of “0.771” means that SENet with hard sampler performs best for the model performance (F1)