Table 4 The test accuracies with different partial fine-tuning strategies for VGG-16.

From: Classification and Morphological Analysis of Vector Mosquitoes using Deep Convolutional Neural Networks

Model

Fine-tuning targets

Accuracy(%)

VGG-16 - M1

All FC layers

76.05

VGG-16 - M2

5th Conv. block + All FC layers

87.26

VGG-16 - M3

4–5th Conv. blocks + All FC layers

88.51

VGG-16 - M4

2–5th Conv. blocks + All FC layers

93.11

VGG-16 - ALL

All Conv. blocks and FC layers

97.19

  1. In all settings, models are initialized with pre-trained weights from the ImageNet dataset. During the training with the ADAM optimizer, the learning rates are set to 5e-6 and reduced by 0.25 every 15 epochs.
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