深度学习框架在计算机视觉问题上的性能评价

Madhumitha Nara, B. Mukesh, Preethi Padala, Bharath A. Kinnal
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引用次数: 3

摘要

近年来,深度学习(DL)的应用激增,并被应用于各个领域。为了使实现更容易,DL框架的开发出现了巨大的增长。在本文中,我们的目标是对gpu加速的深度学习软件框架(如Torch和TenserFlow(使用Keras API))进行比较研究。我们试图通过实现三个不同的神经网络来对这些框架的性能进行基准测试,每个神经网络都是为一个流行的计算机视觉问题(MNIST, CIFAR10, Fashion MNIST)设计的。我们在CPU和GPU(Nvidia GeForce GTX 960M)设置上进行了这个实验。这里使用的性能指标包括评估时间、训练时间和准确性。本文旨在为特定问题选择最合适的框架提供指导。本文的特别兴趣是评估由于使用像Keras这样的API而导致的性能损失,并对用户定义的神经网络和标准网络的性能进行比较研究。我们的兴趣还在于它们在不同规模的网络中的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of Deep Learning frameworks on Computer Vision problems
Deep Learning (DL) applications have skyrocketed in recent years and are being applied in various domains. There has been a tremendous surge in the development of DL frameworks to make implementation easier. In this paper, we aim to make a comparative study of GPU-accelerated deep learning software frameworks such as Torch and TenserFlow (with Keras API). We attempt to benchmark the performance of these frameworks by implementing three different neural networks, each designed for a popular Computer Vision problem (MNIST, CIFAR10, Fashion MNIST). We performed this experiment on both CPU and GPU(Nvidia GeForce GTX 960M) settings. The performance metrics used here include evaluation time, training time, and accuracy. This paper aims to act as a guide to selecting the most suitable framework for a particular problem. The special interest of the paper is to evaluate the performance lost due to the utility of an API like Keras and a comparative study of the performance over a user-defined neural network and a standard network. Our interest also lies in their performance when subjected to networks of different sizes.
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