Image Classification Using TensorFlow GPU

Derrick Yeboah, Mahamat Saleh Adoum Sanoussi, George Kofi Agordzo
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引用次数: 1

Abstract

There are several image classification and a complicated methods that are been overlooked with many articles. This article reviews the latest practices, issues, and options for billing classification. Emphasis is placed on synthesizing important advanced category strategies and targeting strategies that can be used to improve ranking accuracy. Billing sorting is a classic problem in image processing, computer vision, and machine learning. In this article, we study deep learning-based image classification using the TensorFlow GPU. Because the datasets were bridges; CIFAR-10 and MNIST FASHION for the classification module. The results show the efficiency and accuracy of deep learning-based image classification using the TensorFlow GPU. Additionally, some critical issues are mentioned that affect overall performance. However, simple research is needed to identify and reduce uncertainties in the image processing chain to improve classification accuracy.
使用TensorFlow GPU进行图像分类
图像分类的方法和方法比较复杂,很多文章都忽略了这一点。本文回顾了帐单分类的最新实践、问题和选项。重点放在综合重要的先进的类别策略和目标策略,可以用来提高排名的准确性。账单分类是图像处理、计算机视觉和机器学习中的一个经典问题。在本文中,我们使用TensorFlow GPU研究基于深度学习的图像分类。因为数据集是桥梁;CIFAR-10和MNIST FASHION用于分类模块。实验结果表明,基于TensorFlow GPU的深度学习图像分类效率高、准确率高。此外,还提到了一些影响整体性能的关键问题。然而,识别和减少图像处理链中的不确定性,以提高分类精度,还需要简单的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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