Danish Fungi 2020 - Not Just Another Image Recognition Dataset

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE Winter Conference on Applications of Computer Vision, WACV 2022
EditorsLisa O'Conner
Number of pages11
Publication date2022
ISBN (Electronic)978-1-6654-0915-5
Publication statusPublished - 2022
Event22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 - Waikoloa, United States
Duration: 4 Jan 20228 Jan 2022


Conference22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
LandUnited States
SponsorCVF, IEEE Computer Society

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

    Research areas

  • Datasets, Evaluation and Comparison of Vision Algorithms Object Detection/Recognition/Categorization

ID: 326348571