# Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset

arXiv: https://arxiv.org/abs/2007.04690 · HTML: https://mlsanity.com/paper/2007.04690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature](https://mlsanity.com/paper/2501.07171/) (2025)
- [Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images](https://mlsanity.com/paper/2311.11029/) (2023)
- [CHIPS: Efficient CLIP Adaptation via Curvature-aware Hybrid Influence-based Data Selection](https://mlsanity.com/paper/2511.18519/) (2025)
- [Robust Pollen Imagery Classification with Generative Modeling and Mixup Training](https://mlsanity.com/paper/2102.13143/) (2021)
- [Million-scale multimodal pollen microscopy with expert-guided foundation models](https://mlsanity.com/paper/2606.17809/) (2026)

## Abstract (arXiv)

Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented.