Multi-Label Plant Species Classification with Self-Supervised Vision Transformers

• URL: http://arxiv.org/abs/2407.06298v1
• Date: Mon, 8 Jul 2024 18:07:33 GMT
• Title: Multi-Label Plant Species Classification with Self-Supervised Vision Transformers
• Authors: Murilo Gustineli, Anthony Miyaguchi, Ian Stalter,
• Abstract summary: We present a transfer learning approach using a self-supervised Vision Transformer (DINOv2) for the PlantCLEF 2024 competition. To address the computational challenges of the large-scale dataset, we employ Spark for distributed data processing. Our results demonstrate the efficacy of combining transfer learning with advanced data processing techniques for multi-label image classification tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a transfer learning approach using a self-supervised Vision Transformer (DINOv2) for the PlantCLEF 2024 competition, focusing on the multi-label plant species classification. Our method leverages both base and fine-tuned DINOv2 models to extract generalized feature embeddings. We train classifiers to predict multiple plant species within a single image using these rich embeddings. To address the computational challenges of the large-scale dataset, we employ Spark for distributed data processing, ensuring efficient memory management and processing across a cluster of workers. Our data processing pipeline transforms images into grids of tiles, classifying each tile, and aggregating these predictions into a consolidated set of probabilities. Our results demonstrate the efficacy of combining transfer learning with advanced data processing techniques for multi-label image classification tasks. Our code is available at https://github.com/dsgt-kaggle-clef/plantclef-2024.

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