针对芯片图像分类过程中图像数量过少、需要大量人工标注以及效率低的问题，提出一种基于迁移学习的VGG-16网络芯片图像分类方法。该方法通过VGG-16网络直接从原始像素中自动学习图像特征，有效减少人工标注的成本，同时对比了VGG-16网络模型和基于迁移学习的VGG-16网络模型的准确率及其混淆矩阵。实验结果表明，所提出的基于迁移学习的VGG-16网络模型对芯片图像分类效果要优于原VGG-16网络模型。

To solve the problems of lack of images, too much manual marking and low efficiency in the process of chip image classification, a VGG-16 network chip image classification method based on migration learning is proposed. This method is based on the VGG-16 automatic learning in network and can extract directly from the original pixel image characteristics, effectively reducing the cost of manual annotation. In comparison with VGG-16 network model and VGG-16 network model based on transfer learning accuracy and confusion matrix, the experiment results show that the proposed VGG-16 network model based on the migration study on chip image classification effect is better than the original VGG - 16 network model.