首先，本文将介绍卷积神经网络的基本理论和NAS的提出背景及发展过程，简

首先，本文将介绍卷积神经网络的基本理论和NAS的提出背景及发展过程，简单阐述NAS的基本特征，然后又分析以NAS为基础的EfficientNet和RegNet这两种网络模型的结构和特性。第二，为了减少如环境中的光照，背景和形状的变化对实验结果的影响，本文对基础数据集进行了随机翻转、平移、噪声等数据增强处理，增加了样本数据的多样性。

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结果 (英语) 1: [复制]

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First, this article will introduce the basic theory of convolutional neural networks and the background and development process of NAS, briefly explain the basic characteristics of NAS, and then analyze the structure and characteristics of the two network models, EfficientNet and RegNet, based on NAS. <br>Second, in order to reduce the influence of the changes in the environment, such as the lighting, background and shape on the experimental results, this paper has performed data enhancement processing such as random flip, translation, and noise on the basic data set, which increases the diversity of sample data.

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结果 (英语) 2:[复制]

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First of all, this paper will introduce the basic theory of reel neural networks and the background and development process of NAS, briefly expound the basic characteristics of NAS, and then analyze the structure and characteristics of the two network models based on NAS, EfficientNet and RegNet.<br>Second, in order to reduce the effect of lighting in the environment, background and shape changes on the experimental results, this paper has randomly flipped, panned, noise and other data enhancement processing of the basic data set, increasing the diversity of sample data.

正在翻译中..

结果 (英语) 3:[复制]

复制成功！

Firstly, this paper introduces the basic theory of convolutional neural network, the background and development process of NAS, briefly expounds the basic characteristics of NAS, and then analyzes the structure and characteristics of the two network models, namely, efficientnet and regnet based on NAS.<br>Secondly, in order to reduce the influence of illumination, background and shape changes on experimental results, this paper makes some data enhancement processing, such as random flipping, translation and noise, which increases the diversity of sample data.<br>

正在翻译中..

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