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Efficient Classification of Image Using Transfer Learning With Fine Tunning

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The approach achieved a validation accuracy of 95.42% after fine-tuning the VGG16 model.
Freezing initial layers and training later convolutional blocks led to reduced training time and resource usage.
The methodology involved loading pre-trained models, customizing dense layers, and training over five epochs.
Insights into model performance were provided through loss curves, accuracy plots, and a confusion matrix.

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This research explores transfer learning and fine-tuning using VGG16 for binary image classification, highlighting accuracy and resource efficiency.

Vishwa Patel (2017) studied this question.

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This research explores transfer learning and fine-tuning using VGG16 for binary image classification, highlighting accuracy and resource efficiency.