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Transfer Learning with Self-Supervised Vision Transformers for Snake Identification

Year: 2024

Type: preprint

Abstract: We present our approach for the SnakeCLEF 2024 competition to predict snake species from images. We explore and use Meta's DINOv2 vision transformer model for feature extraction to tackle species' high variability and visual similarity in a dataset of 182,261 images. We perform exploratory analysis on embeddings to understand their structure, and train a linear classifier on the embeddings to predict species. Despite achieving a score of 39.69, our results show promise for DINOv2 embeddings in snake identification. All code for this project is available at https://github.com/dsgt-kaggle-clef/snakeclef-2024.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2407.06178

Authors: Miyaguchi, Anthony, Gustineli, Murilo, Fischer, Austin, Lundqvist, Ryan

Institution: Georgia Institute of Technology

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4400488392

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