Object Detection

Dinh, Kevin; Dinh, Brian; Leavitt, Andrew; Tran, Annie

Object Detection

dc.contributor.author	Dinh, Kevin	en
dc.contributor.author	Dinh, Brian	en
dc.contributor.author	Leavitt, Andrew	en
dc.contributor.author	Tran, Annie	en
dc.date.accessioned	2023-03-10T18:02:31Z	en
dc.date.available	2023-03-10T18:02:31Z	en
dc.date.issued	2022-12-15	en
dc.description.abstract	For this project, our team took 20,000 image samples from ETDs and annotated them using a Python package called PyLabel. PyLabel is an open-source Python library used to label PDFs. PyLabel can also take a trained dataset and use it for AI-aided annotations. We also created a pipeline in order to divide the dataset into equal pieces, where a user can select the number of samples they want to annotate. Then old sample data is cleared out and replaced with new sample data that contains classes with low accuracy. Finally, we saved the annotations as a YOLOv7 .txt file which is accumulated in order to retrain the model with 10,000 annotated images and finally with 20,000 annotated pages. With these annotated pages we conducted an experiment timing how long it takes to annotate the pages to see the improvement of the average time per page to annotate as the different models were trained. We concluded that the model trained with 10,000 pages was significantly faster than the original model.	en
dc.description.notes	ObjectDetectionReport.pdf - Final Report (PDF Version) ObjectDetectionReport.docx - Final Report (Word Version) ObjectDetectionPresentation.pdf - Final Presentation (PDF Version) ObjectDetectionPresentation.pptx - Final Presentation (PowerPoint Version)	en
dc.identifier.uri	http://hdl.handle.net/10919/114082	en
dc.language.iso	en_US	en
dc.publisher	Virginia Tech	en
dc.rights	CC0 1.0 Universal	en
dc.rights.uri	http://creativecommons.org/publicdomain/zero/1.0/	en
dc.subject	Machine Learning	en
dc.subject	Labeling	en
dc.subject	Object Detection	en
dc.title	Object Detection	en
dc.type	Presentation	en
dc.type	Report	en