Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images

• URL: http://arxiv.org/abs/2407.08027v1
• Date: Wed, 10 Jul 2024 20:10:56 GMT
• Title: Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
• Authors: Kazi Sajeed Mehrab, M. Maruf, Arka Daw, Harish Babu Manogaran, Abhilash Neog, Mridul Khurana, Bahadir Altintas, Yasin Bakis, Elizabeth G Campolongo, Matthew J Thompson, Xiaojun Wang, Hilmar Lapp, Wei-Lun Chao, Paula M. Mabee, Henry L. Bart Jr., Wasila Dahdul, Anuj Karpatne,
• Abstract summary: The Fish-Visual Trait Analysis dataset is a large, annotated collection of about 60K fish images spanning 1900 different species. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images.
• Score: 19.539428709275917
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fishes are integral to both ecological systems and economic sectors, and studying fish traits is crucial for understanding biodiversity patterns and macro-evolution trends. To enable the analysis of visual traits from fish images, we introduce the Fish-Visual Trait Analysis (Fish-Vista) dataset - a large, annotated collection of about 60K fish images spanning 1900 different species, supporting several challenging and biologically relevant tasks including species classification, trait identification, and trait segmentation. These images have been curated through a sophisticated data processing pipeline applied to a cumulative set of images obtained from various museum collections. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images, facilitating additional trait segmentation and localization tasks. The ultimate goal of Fish-Vista is to provide a clean, carefully curated, high-resolution dataset that can serve as a foundation for accelerating biological discoveries using advances in AI. Finally, we provide a comprehensive analysis of state-of-the-art deep learning techniques on Fish-Vista.

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