Multi-Label Plant Species Classification with Self-Supervised Vision Transformers
<|reference_start|>Multi-Label Plant Species Classification with Self-Supervised Vision Transformers: 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.<|reference_end|>
@article{gustineli2024multi-label,
    title={Multi-Label Plant Species Classification with Self-Supervised Vision
  Transformers},
    author={Murilo Gustineli, Anthony Miyaguchi, and Ian Stalter},
    journal={arXiv preprint arXiv:2407.06298},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06298},
    primaryClass={cs.CV cs.IR cs.LG}
}