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Spider species classification using Vision Transformers and Convolutional Neural Networks

Grant number: 22/14286-2
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): January 01, 2023
Effective date (End): December 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Nina Sumiko Tomita Hirata
Grantee:Arthur Teixeira Magalhães
Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:15/22308-2 - Intermediate representations in Computational Science for knowledge discovery, AP.TEM


Spiders often seek shelter in the heat and safety of homes and although most of them are harmless, some can represent a real danger. Thus, since differentiating spider species can be a challenge for people with no prior knowledge, having a way to identify them could be useful to avoid those that are poisonous. To solve this question, in this project we propose to study and compare convolutional neural networks (CNN) and vision transformers (ViT) regarding the performance in the task of recognizing spider species from their images. These techniques perform automatic feature extraction and learn high-level representations of images, and are currently widely used, for example, in object detection and image classification. Specifically, we aim to evaluate the potential of these networks for the task of classifying spider species, including quantitative and qualitative performance comparisons between the two types of networks. For this, we will use INaturalist's arachnid photo database. The results of this project may contribute to the development of applications aimed at identifying spiders and providing information of interest about the species.

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