Leveraging synthetic data produced from museum specimens to train adaptable species classification models.

ABSTRACT: Computer vision has increasingly shown potential to improve data processing efficiency in ecological research. However, training computer vision models requires large amounts of high-quality, annotated training data. This poses a significant challenge for researchers looking to create bespoke computer vision models, as substantial human resources and biological replicates are often needed to adequately train these models. Synthetic images have been proposed as a potential solution for generating large training datasets, but models trained with synthetic images often have poor generalization to real photographs. Here we present a modular pipeline for training generalizable classification models using synthetic images. Our pipeline includes 3D asset creation with the use of 3D scanners, synt

SUBMITTER: Blair JD 

PROVIDER: S-EPMC12407421 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature