dc.contributor.authorMundhra, Shreyas Sudhir
dc.date.accessioned2017-04-24T09:24:28Z
dc.date.available2017-04-24T09:24:28Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/70465
dc.description.abstractDue to the emergence and rise of smartphones and other forms of embedded computing in today's times, it has become important to find object detection algorithms that are less resource intensive. In this project, I have tried to analyse some of the reasons existing algorithms for object detection are GPU intensive and have tried to implement an algorithm for car localization using neural networks that is also GPU efficient. Due to limited availability of GPU resources, it was not feasible to train the model from scratch. Hence, we have used transfer learning techniques by reusing some of the weights of some known models which are used for similar tasks.en_US
dc.format.extent40 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleCar detection using transfer learning methodologyen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorPan Jialin, Sinnoen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US


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