Transfer Learning with Self-Supervised Vision Transformers for Snake Identification

2024/07/08 by Anthony Miyaguchi, Murilo Gustineli, Miyaguchi, Anthony +5 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Robot Manipulation and Learning

paper · pdf · doi:10.48550/arxiv.2407.06178

openalex publication_date 2024/07/08 · openalex created_date 2024/07/11 · openalex updated_date 2026/07/28

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.

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• DS@GT AnimalCLEF: Triplet Learning over ViT Manifolds with Nearest Neighbor Classification for Animal Re-identification

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