Class-wise F1, RC, and PR metrics for FishSpecies classification using SwinFishNet across 5-FCV.

Class-wise F1, RC, and PR metrics for FishSpecies classification using SwinFishNet across 5-FCV.

<p>Class-wise F1, RC, and PR metrics for FishSpecies classification using SwinFishNet across 5-FCV.</p>

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Bibliographic Details
Main Author:	Ebru Ergün (21395498) (author)
Published:	2025
Subjects:	Ecology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified matthews correlation coefficient hierarchical structure enhances edge architecture known confusion matrix metrics computer vision tasks 224 224 pixels deep learning models ca ), f1 global seafood trade div >< p optimizing market efficiency class fishspecies dataset improving food safety class smallfishbd dataset food safety fishspecies dataset market efficiency transfer learning seafood industry improving sustainability global features fish dataset class bd work offers unique ability tensor format swinfishnet ), swin transformer significant contributions results underscore research settings novel methodology model ’ innovative approach image processing fish market ensuring sustainability domestic economies crucial industry cohen ’ broad applications based classification based approach artificial intelligence applied using adamw algorithm
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