Fungi recognition: A practical use case
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Accepted author manuscript, 1.7 MB, PDF document
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.
|Title of host publication||Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020|
|Number of pages||9|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2020|
|Event||2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States|
Duration: 1 Mar 2020 → 5 Mar 2020
|Conference||2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020|
|Periode||01/03/2020 → 05/03/2020|
|Sponsor||CVF, IEEE Computer Society|
|Series||Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020|
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