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Transfer Learning: Pre-Trained vs Fine-Tuning by publisherjff is a document available to read on EtoBox.

What is Transfer Learning: Pre-Trained vs Fine-Tuning about?

This paper compares pre-trained and fine-tuning methodologies in transfer learning for image classification, specifically classifying cats and dogs. The study demonstrates that fine-tuning a pre-trained MobileNet V2 model significantly improves accuracy, achieving 98% validation accuracy compared to 95.14% with feature extraction alone. The results indicate that fine-tuning allows for better adaptation of the model to specific tasks by leveraging learned features from a large dataset.

Author

publisherjff

Language

EN