The Herbarium Challenge 2019 Dataset

Abstract

Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in computer vision and deep learning, developing an automated approach to help experts identify specimens could significantly accelerate research in this area. Whereas most existing botanical datasets comprise photos of specimens in the wild, herbarium sheets exhibit dried specimens, which poses new challenges. We present a challenge dataset of herbarium sheet images labeled by experts, with the intent of facilitating the development of automated identification techniques for this challenging scenario.

Part of the 6th Fine-Grained Visual Categorization Workshop (FGVC6) at CVPR 2019. Dataset available at https://github.com/visipedia/herbarium_comp

References in corpus (2)

• Random Erasing Data Augmentation
• Class-Balanced Loss Based on Effective Number of Samples

Cited by in corpus (4)

• Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention
• Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review
• The Herbarium 2021 Half-Earth Challenge Dataset
• Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity