Multi-Label Plant Species Classification with Self-Supervised Vision Transformers
This work addresses the problem of efficient and accurate multi-label classification of plant species for ecological and agricultural applications, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled multi-label plant species classification for the PlantCLEF 2024 competition by using a self-supervised Vision Transformer (DINOv2) with transfer learning and distributed data processing via Spark, achieving effective results in handling large-scale datasets.
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.