Abstract:

In recent years, with the continuous expansion of data sets and the continuous improvement of computer performance, the traditional image classification methods always lead to low accuracy. Because of their high accuracy and great convergence, the residual networks have become a key technical in the field of image classification, they are worthy of studying in detail. Each variant improves network performance by improving classification accuracy, reducing model complexity or reducing calculation amount. Firstly, This paper analyzes the advantages and disadvantages of each variant and the suggestions are given for the application of them. Then, the performance of each variant is compared intuitively from the three aspects of accuracy rate, parameter amount and calculation amount. Finally, the challenges and future development of residual networks are put forward.

Key words: residual network, depth neural network, image classification, computer vision

摘要：

近年来，随着数据量的扩大，计算机性能不断提升，用传统的图像分类方法无法获得大数据下图像分类的高精度准确率，残差网络因其高度准确性和良好收敛性已成为图像分类领域的技术热点，值得深入研究。介绍了残差网络，并从提升分类准确率、减少模型参数量和降低模型计算量三个角度出发，详细讨论了各个变体的内在结构，分析了各个变体的优缺点，给出了各个变体适用场合的建议。从准确率、参数量和计算量三个方面对各个变体的性能进行了直观的对比。提出了残差网络现在面临的挑战和未来的发展方向。

关键词: 残差网络, 深度神经网络, 图像分类, 计算机视觉