Image collection of 3D-printed prototypes and non-3D-printed prototypes

Matthew Li, Ninad Mahajan, Jessica Menold, Christopher McComb

Research output: Contribution to journalArticle

Abstract

Image processing refers to the use of computer algorithms to manipulate and enhance digital images to improve their quality or to make them more suitable for tasks such as classification. Common benchmarking datasets in this field include the imagenet, CIFAR-100, and MNIST datasets. This dataset is a collection of images that are particularly relevant to engineering and design, consisting of two categories: 3D-printed prototypes, and non-3D-printed prototypes This data was collected through a hybrid approach that entailed both web scraping and direct collection from engineering labs and workspaces at Penn State University. The initial data was then augmented using several data augmentation techniques including rotation, noise, blur, and color shifting. This dataset is potentially useful to train image classification algorithms or attentional mapping approaches. This data can be used either by itself or used to bolster an existing image classification dataset.

Original languageEnglish (US)
Article number104691
JournalData in Brief
Volume27
DOIs
StatePublished - Dec 2019

All Science Journal Classification (ASJC) codes

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