Deep learning convolutional network for image classification
Deep learning architecture algorithms have been extensively developed and applied to various applications. The techniques have successfully improved the performance of difficult computer tasks such as computer vision, natural language processing, and speech recognition. This project aims to build an...
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2019
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sg-ntu-dr.10356-780322023-03-04T18:43:00Z Deep learning convolutional network for image classification Moektijono, Isselin Tegoeh Tjahjowidodo School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Deep learning architecture algorithms have been extensively developed and applied to various applications. The techniques have successfully improved the performance of difficult computer tasks such as computer vision, natural language processing, and speech recognition. This project aims to build and apply one of the well-known deep learning algorithms, Convolutional Neural Network to detect and classify different defects on manufacture parts, which categorized under image classification problem. The input data which is in the form of two-dimensional file of images which will be fed into various training models. The trained model reached a considered decent training accuracy result and could be used as foundation model to be applied for live prediction on video feed data. Bachelor of Engineering (Mechanical Engineering) 2019-06-11T05:17:21Z 2019-06-11T05:17:21Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78032 en Nanyang Technological University 63 p. application/pdf |
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DRNTU::Engineering::Mechanical engineering Moektijono, Isselin Deep learning convolutional network for image classification |
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Deep learning architecture algorithms have been extensively developed and applied to various applications. The techniques have successfully improved the performance of difficult computer tasks such as computer vision, natural language processing, and speech recognition. This project aims to build and apply one of the well-known deep learning algorithms, Convolutional Neural Network to detect and classify different defects on manufacture parts, which categorized under image classification problem. The input data which is in the form of two-dimensional file of images which will be fed into various training models. The trained model reached a considered decent training accuracy result and could be used as foundation model to be applied for live prediction on video feed data. |
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Tegoeh Tjahjowidodo |
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Tegoeh Tjahjowidodo Moektijono, Isselin |
format |
Final Year Project |
author |
Moektijono, Isselin |
author_sort |
Moektijono, Isselin |
title |
Deep learning convolutional network for image classification |
title_short |
Deep learning convolutional network for image classification |
title_full |
Deep learning convolutional network for image classification |
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Deep learning convolutional network for image classification |
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Deep learning convolutional network for image classification |
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deep learning convolutional network for image classification |
publishDate |
2019 |
url |
http://hdl.handle.net/10356/78032 |
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1759854268516401152 |