Fungi Recognition: A Practical Use Case
Abstract
The paper presents a system for visual recognition of 1394 fungi species based on deep convolutional neural networks and its deployment in a citizen-science project. The system allows users to automatically identify observed specimens, while providing valuable data to biologists and computer vision researchers. The underlying classification method scored first in the FGVCx Fungi Classification Kaggle competition organized in connection with the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2018. We describe our winning submission and evaluate all technicalities that increased the recognition scores, and discuss the issues related to deployment of the system via the web- and mobile- interfaces.
Cite
Text
Sulc et al. "Fungi Recognition: A Practical Use Case." Winter Conference on Applications of Computer Vision, 2020.Markdown
[Sulc et al. "Fungi Recognition: A Practical Use Case." Winter Conference on Applications of Computer Vision, 2020.](https://mlanthology.org/wacv/2020/sulc2020wacv-fungi/)BibTeX
@inproceedings{sulc2020wacv-fungi,
title = {{Fungi Recognition: A Practical Use Case}},
author = {Sulc, Milan and Picek, Lukas and Matas, Jiri and Jeppesen, Thomas and Heilmann-Clausen, Jacob},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2020},
url = {https://mlanthology.org/wacv/2020/sulc2020wacv-fungi/}
}