What is it about?
This paper presents Marine-tree, a large-scale hierarchical annotated dataset for marine organism classification. Marine-tree contains 161,180 annotated images divided into 60 classes organised in a hierarchy-tree structure, Images were meticulously collected by scuba divers using the RLS (Reef Life Survey) methodology and later annotated by experts in the field. Marine-tree is distinguished from other already available marine datasets by their flexibility and abundance of images and classes organized in a deep hierarchy.
Featured Image
Photo by Adarsh Kummur on Unsplash
Why is it important?
Large-scale hierarchical annotated dataset for marine organism classification which contains 161,180 annotated images divided into 60 classes
Perspectives
I hope Marine-tree dataset will bring the researcher to focus more on hierarchical image classification. The research article provides large scale hierarchical annotated dataset for marine organism classification Besides, we have presented hierarchical loss function that can be applied to any multi-level hierarchical model which takes into account the parent-child relationship between predictions and uses it to penalize the loss in a case where there is an inconsistency.
Imran Razzak
University of New South Wales
Read the Original
This page is a summary of: Marine-tree:
A Large-scale Marine Organisms Dataset for Hierarchical Image Classification, October 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3511808.3557634.
You can read the full text:
Contributors
The following have contributed to this page
