Fungi Recognition: A Practical Use Case

Milan Sulc, Lukas Picek, Jiri Matas, Thomas Jeppesen, Jacob Heilmann-Clausen; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 2316-2324

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.

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[bibtex]
@InProceedings{Sulc_2020_WACV,
author = {Sulc, Milan and Picek, Lukas and Matas, Jiri and Jeppesen, Thomas and Heilmann-Clausen, Jacob},
title = {Fungi Recognition: A Practical Use Case},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}