Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9869
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dc.contributor.authorSingh, Harshit-
dc.contributor.authorDogra, Archit-
dc.contributor.authorModi, Praveen [Guided by]-
dc.date.accessioned2023-09-07T10:14:07Z-
dc.date.available2023-09-07T10:14:07Z-
dc.date.issued2023-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9869-
dc.descriptionEnrolment No. 191264, 191277en_US
dc.description.abstractThere’s a common adage that data scientists spend 90% of their time cleaning data and 10% modelling. With image classifiers, it is more like 99% cleaning to 1% modelling. This is because a neural network needs images to be of a standardized size. How many pictures do we come across on a google image search that are all the same size? There are a bevy of different approaches for standardizing images and it is important to remember that no method is necessarily better or worse than another. Each one has its own drawbacks and applications. Oftentimes your ultimate limiter will be computer power.en_US
dc.language.isoen_USen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectData cleaningen_US
dc.subjectEye image dataseten_US
dc.titleData Cleaning in Eye Image Dataseten_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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