基于深度卷积神经网络的Cifar-10分类

2018 IEEE Punecon Pub Date : 2018-11-01 DOI:10.1109/PUNECON.2018.8745428

R. Doon, Tarun Kumar Rawat, S. Gautam

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引用次数: 28

摘要

卷积神经网络等深度学习模型已经在图像分类和目标检测任务中取得了成功。本文使用Cifar-10数据集对我们的深度学习模型进行基准测试。利用Adam、RMS等多种函数优化方法以及各种正则化技术，对图像分类任务获得了较好的精度。

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Cifar-10 Classification using Deep Convolutional Neural Network

Deep learning models such as convolution neural networks have been successful in image classification and object detection tasks. Cifar-10 dataset is used in this paper to benchmark our deep learning model. Various function optimization methods such as Adam, RMS along with various regularization techniques are used to get good accuracy on image classification task.

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来源期刊

2018 IEEE Punecon

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