Research and Application of Deep Learning Based on Transfer Learning in Image Classification Tasks

With the continuous improvement of big data and computing power, deep learning models have achieved remarkable results in the field of image recognition, but building and training a deep neural network from scratch often requires a large amount of annotated data and expensive computing resources. This article first outlines the basic principles and challenges of deep learning in image classification tasks. Especially for task scenarios with small samples or scarce annotations, traditional deep learning models are prone to overfitting and insufficient generalization performance. Transfer learning is introduced into this study as an important strategy. Through deep models (such as ResNet, VGG, etc.) pre-trained on large-scale image data sets (such as ImageNet), universal feature representations are extracted. And we transfer these pre-trained model parameters to specific target image classification tasks for fine-tuning. Furthermore, this article elaborates on several typical applications of transfer learning in deep learning models, and analyzes how transfer learning can effectively help improve the accuracy of image classification on the target data set based on actual cases. The experimental part compares the results of directly training a new model and using the transfer learning method to initialize the model and then train on a variety of target data sets. The experiment proves that transfer learning can significantly improve the learning efficiency and final classification performance of the model under limited samples.