Summary of Multi-label Plant Species Classification with Self-supervised Vision Transformers, by Murilo Gustineli et al.
Multi-Label Plant Species Classification with Self-Supervised Vision Transformers
by Murilo Gustineli, Anthony Miyaguchi, Ian Stalter
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a novel approach to plant species classification, leveraging the Vision Transformer (DINOv2) in a transfer learning setting for the PlantCLEF 2024 competition. The method combines self-supervised learning with multi-label classification, utilizing both base and fine-tuned DINOv2 models to extract rich feature embeddings. To address computational challenges posed by the large-scale dataset, Spark is employed for distributed data processing, ensuring efficient memory management and processing across a cluster of workers. The approach transforms images into grids of tiles, classifying each tile, and aggregating predictions into consolidated probabilities. Results demonstrate the efficacy of combining transfer learning with advanced data processing techniques for multi-label image classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to use a special kind of artificial intelligence called Vision Transformer (DINOv2) to identify different types of plants in pictures. The goal is to be able to recognize multiple plant species within a single image. To make this possible, the researchers used a technique called transfer learning, which allows them to adapt the AI model for specific tasks. They also developed a way to process large amounts of data efficiently using Spark. This approach can help with multi-label image classification tasks and has potential applications in fields like agriculture or conservation. |
Keywords
» Artificial intelligence » Classification » Image classification » Self supervised » Transfer learning » Vision transformer
