| Summary: | 碩士 === 大葉大學 === 電機工程學系 === 106 === In this paper, we conduct a comparison of transfer learning and fine-tuning on the performance of vehicle classification in a relatively small dataset. For deep learning-based classification task, sufficient training data are very important, but sometimes the collection of training data is quite difficult, especially for medical images. Therefore, the investigation of deep learning-based classification problem for a relatively small dataset is still valuable. Transfer learning is a method that uses a pre-trained deep convolutional (CONV) neural network to learn patterns from data that are not seen before, which is often served as a feature extractor. As for fine-tuning, it can be considered as another type of transfer learning, but its performance is usually better than transfer learning, provided there is sufficient training data. Experimental results show that for transfer learning, the average recall rates are all the same of 93% when either the classifier of linear SVM or Logistic Regression is applied on the top of the network architecture. But for fine-tuning, the average recall rate can be further increased from 93% of transfer learning to 95%, indicating that fine-tuning outperforms transfer learning on the task of vehicle classification.
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