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

arXiv: https://arxiv.org/abs/2407.06298 · HTML: https://mlsanity.com/paper/2407.06298/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)](https://mlsanity.com/paper/1610.02455/) (2016)
- [[CLS] Token is All You Need for Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2304.06212/) (2023)

## Built on by (most cited first, 7 of 7)

- [Overview of PlantCLEF 2024: multi-species plant identification in vegetation plot images](https://mlsanity.com/paper/2509.15768/) (2025)
- [Tile-Based ViT Inference with Visual-Cluster Priors for Zero-Shot Multi-Species Plant Identification](https://mlsanity.com/paper/2507.06093/) (2025)
- [Multi-Label Plant Species Prediction with Metadata-Enhanced Multi-Head Vision Transformers](https://mlsanity.com/paper/2508.10457/) (2025)
- [AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock](https://mlsanity.com/paper/2507.22101/) (2025)
- [Label-efficient underwater image classification with logistic regression on frozen foundation model embeddings](https://mlsanity.com/paper/2604.00313/) (2026)
- [Inference-Path Optimization via Circuit Duplication in Frozen Visual Transformers for Marine Species Classification](https://mlsanity.com/paper/2604.03428/) (2026)
- [Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification](https://mlsanity.com/paper/2607.14509/) (2026)

## Abstract (arXiv)

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.