Danish Fungi 2020 - Not Just Another Image Recognition Dataset

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966004" target="_blank" >RIV/49777513:23520/22:43966004 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/22:00362997

Result on the web

<a href="https://openaccess.thecvf.com/content/WACV2022/html/Picek_Danish_Fungi_2020_-_Not_Just_Another_Image_Recognition_Dataset_WACV_2022_paper.html" target="_blank" >https://openaccess.thecvf.com/content/WACV2022/html/Picek_Danish_Fungi_2020_-_Not_Just_Another_Image_Recognition_Dataset_WACV_2022_paper.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WACV51458.2022.00334" target="_blank" >10.1109/WACV51458.2022.00334</a>

Result language

angličtina

Original language name

Danish Fungi 2020 - Not Just Another Image Recognition Dataset

Original language description

We introduce a novel fine-grained dataset and bench-mark, the Danish Fungi 2020 (DF20). The dataset, constructed from observations submitted to the Atlas of Danish Fungi, is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, and well-defined class hierarchy. DF20 has zero overlap with ImageNet, al-lowing unbiased comparison of models fine-tuned from publicly available ImageNet checkpoints. The proposed evaluation protocol enables testing the ability to improve classification using metadata - e.g. precise geographic location, habitat, and substrate, facilitates classifier calibration testing, and finally allows to study the impact of the device settings on the classification performance. Experiments using Convolutional Neural Networks (CNN) and the recent Vision Transformers (ViT) show that DF20 presents a challenging task. Interestingly, ViT achieves results su-perior to CNN baselines with 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and 12% respectively. A simple procedure for including metadata into the decision process improves the classification accuracy by more than 2.95 percentage points, reducing the error rate by 15%. The source code for all methods and experiments is available at https://sites.google.com/view/danish-fungi-dataset.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20205 - Automation and control systems

Project

<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022

ISBN

978-1-66540-915-5

ISSN

2472-6737

e-ISSN

2642-9381

Number of pages

11

Pages from-to

3281-3291

Publisher name

IEEE

Place of publication

New York

Event location

Waikoloa, Hawai, Spojené státy

Event date

Jan 4, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000800471203036