基于卷积网络的大数据分类研究

Research on Big Data Classification Based on Convolutional Network

卷积网络由于其出色的性能,在计算机视觉领域被广泛使用。尤其在提倡工业智能化、无人化的时代背景下,基于卷积网络的计算机视觉项目得到了极大的发展。但是由于卷积网络其自身特性所限制,网络模型常常出现所需数据量大、模型训练困难等问题。并且,在实际项目中,由于机器成本、重量等限制,对模型是否可以在低成本、低性能环境下保持可用性能提出了很高的要求。在大数据分类方面,本文结合卷积网络,对其分类情况加以研究。

Convolutional networks are widely used in the field of computer vision due to their excellent performance.Especially in the era of advocating industrial intelligence and unmanned operations,computer vision projects based on convolutional networks have been greatly developed.However,due to the limitations of convolutional networks,network models often have problems such as mass data required and difficult model training.Moreover,in actual projects,due to the limitations of machine cost and weight,high requirements are put forward for whether the model can maintain usable performance in a low-cost,low-performance environment.In terms of big data classification,this paper combines convolutional networks to study their classification.