BENCHMARKING AND CREATION OF A DEEP LEARNING COMPUTER VISION DATASET IN THE TAXONOMIC REVISION OF THE PALO SANTO TREE HERBARIUM SPECIMENS
type: CreativeWork
description: Premise of the studyHerbarium collections have historically been used as a manual data source for taxonomies and there are large collections that have been digitized since the start of the 21st century. Few have been curated for computer vision problems beyond image classification, and even fewer are available to the public. In this study, I investigated the steps and effort it takes both human and computer to create a publically-available data set that can be used for image classification, object detection, and segmentation. Secondly, while benchmarking the data set, I investigated the relationship between the algorithm class, data augmentation, and network size. Our hypothesis for the first research question is that, despite the tools and technology out there to help expedite the process, that it still requires a substantial amount of human effort to complete the task. Secondly, I believe that there will be signals within the data, and that there is a relationship between the class of algorithm, data augmentation, and network size. Methods: A team of three people, one botanist, a data scientist, and research assistant contributed to the creation of this benchmark data set. Data was collected both from historical and new field samples and digitized in Washington D.C. B. graveolens and B. penicillata were selected as the benchmarking species, as they are rare tree species to reduce variability and ensure and were able to collect most of the known herbarium samples around the world. Supervisely, a web-based tool to scale image annotations, was used to annotate objects such as plant features, and to track efforts [1]. Python 3.7 was used for post- processing and creation of the data sets from Supervisely [2]. Descriptive statistics for univariate and bivariate were conducted using mean (sd), frequency counts, and appropriate statistical graphing. Deep learning models were created via PyTorch and torchvision for classification (B.graveolens and B. penicillata), semanti…
abstract: Premise of the studyHerbarium collections have historically been used as a manual data source for taxonomies and there are large collections that have been digitized since the start of the 21st century. Few have been curated for computer vision problems beyond image classification, and even fewer are available to the public. In this study, I investigated the steps and effort it takes both human and computer to create a publically-available data set that can be used for image classification, object detection, and segmentation. Secondly, while benchmarking the data set, I investigated the relationship between the algorithm class, data augmentation, and network size. Our hypothesis for the first research question is that, despite the tools and technology out there to help expedite the process, that it still requires a substantial amount of human effort to complete the task. Secondly, I believe that there will be signals within the data, and that there is a relationship between the class of algorithm, data augmentation, and network size. Methods: A team of three people, one botanist, a data scientist, and research assistant contributed to the creation of this benchmark data set. Data was collected both from historical and new field samples and digitized in Washington D.C. B. graveolens and B. penicillata were selected as the benchmarking species, as they are rare tree species to reduce variability and ensure and were able to collect most of the known herbarium samples around the world. Supervisely, a web-based tool to scale image annotations, was used to annotate objects such as plant features, and to track efforts [1]. Python 3.7 was used for post- processing and creation of the data sets from Supervisely [2]. Descriptive statistics for univariate and bivariate were conducted using mean (sd), frequency counts, and appropriate statistical graphing. Deep learning models were created via PyTorch and torchvision for classification (B.graveolens and B. penicillata), semanti…
author: Valko, Matthew
keywords: Artificial Intelligence, Computer Vision, Deep Learning, Herbarium, Systematics, Taxonomy
date published: 2024
date modified: 2026-07-27T19:51:58Z
provider: George Mason University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: George Mason University