论文标题
常规深度学习模型的经验性能分析，用于识别二维图像中对象的识别
Empirical Performance Analysis of Conventional Deep Learning Models for Recognition of Objects in 2-D Images
论文作者
论文摘要
人工神经网络是深度学习的重要组成部分，源自人脑的结构和功能。它具有从医学分析到自动驾驶的广泛应用。在过去的几年中，深度学习技术已大大改进 - 现在可以通过改变网络体系结构，网络参数等来更大程度地定制模型。我们拥有各种参数，例如学习率，滤波器大小，隐藏层的数量，步幅大小和激活功能，以分析模型的性能，从而产生具有最高性能的模型。该模型将图像分为3个类别，即汽车，面部和飞机。
Artificial Neural Networks, an essential part of Deep Learning, are derived from the structure and functionality of the human brain. It has a broad range of applications ranging from medical analysis to automated driving. Over the past few years, deep learning techniques have improved drastically - models can now be customized to a much greater extent by varying the network architecture, network parameters, among others. We have varied parameters like learning rate, filter size, the number of hidden layers, stride size and the activation function among others to analyze the performance of the model and thus produce a model with the highest performance. The model classifies images into 3 categories, namely, cars, faces and aeroplanes.
