Transfer Learning and Fine-Tuning for Deep Learning-Based Tea Diseases Detection on Small Datasets

Abstract

It is well-known that a large amount of data is required to train deep learning systems. However, data collection is very costly if it is not impossible to do. To overcome the limited data problem, one can use models that have been trained with a large dataset and apply them in the target domain with a limited dataset. In this paper, we use pre-trained models on imageNet data and re-train them on our data to detect tea leaf diseases. Those pre-trained models use deep convolutional neural network (DCNN) architectures: VGGNet, ResNet, and Xception. To mitigate the difference tasks of ImageNet and ours, we apply fine-tuning on the pre-trained models by replacing some parts of the pre-trained models with new structures. We evaluate the performance using various re-training and fine-tuning schema. The vanilla pre-trained model is used as the baseline while other techniques such as re-training the models on the appended structures, partially re-training the pre-trained models, and fully re-training the whole networks where the pre-trained models are used in the initialization as the evaluator. Our experiments show that applying transfer learning only on our data may not be effective due to the difference in our task to ImageNet. Applying fine-tuning on pre-trained DCNN models is found to be effective. It is consistently better than that of using transfer learning only or partial fine-tuning. It is also better than training the model from scratch, i.e., without using pre-trained models.