Figure 3
A bar graph compares the accuracy of different convolutional neural network models using various fine-tuning strategies. The horizontal axis lists the models: V G G 16, ResNet50V2, MobileNetV2, and InceptionV3. The vertical axis represents accuracy, ranging from 0.0 to 1.0. There are three sets of bars for each model, representing different fine-tuning strategies: Baseline, Freeze Early, and Freeze Late. The Baseline bars are dark gray, Freeze Early bars are medium gray, and Freeze Late bars are light gray. For V G G 16, the Baseline accuracy is approximately 0.8, Freeze Early is slightly higher, and Freeze Late is around 0.7. For ResNet50V2, the Baseline accuracy is around 0.85, Freeze Early is slightly higher, and Freeze Late is around 0.8. For MobileNetV2, the Baseline accuracy is around 0.8, Freeze Early is slightly higher, and Freeze Late is around 0.75. For InceptionV3, the Baseline accuracy is around 0.9, Freeze Early is slightly higher, and Freeze Late is around 0.85.Comparison of baseline and task-specific fine-tuning strategies across CNN models (VGG16, ResNet50V2, MobileNetV2 and InceptionV3)