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|Title:||Image classification with various deep learning architectures||Authors:||Liu, Hexin||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2018||Abstract:||The goal of the image classification is to correctly predict the subject of an image. For this project, because of the restrictions on resources and time, we worked by using a smaller dataset called Tiny ImageNet, then attempted to train an image classifier using this data. This project implemented some famous Convolutional Neural Networks with various useful techniques. The deep learning architectures we implemented in this project include AlexNet, GoogLeNet, ResNet and DenseNet and their several different versions, the techniques we applied in this project include dropout, data augmentation, weight decay and snapshot ensembles and cyclic learning rates. Consequently, we compared the performance of them in image classification to get the best one with the highest accuracy.||URI:||http://hdl.handle.net/10356/76031||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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