BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data
The BengalDeltaFish [1] dataset resolves the common challenge of recognizing fish species in real-world fish market environments by providing a diverse, large-scale image collection of different fish species captured under uncontrolled and realistic conditions. Unlike traditional fish datasets that...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925004913 |
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| author | Sabrina Alim Dipa Arjun Pal Md. Shoaib Shahria Md. Delwar Shahadat Deepu Raiyan Rahman |
| author_facet | Sabrina Alim Dipa Arjun Pal Md. Shoaib Shahria Md. Delwar Shahadat Deepu Raiyan Rahman |
| author_sort | Sabrina Alim Dipa |
| collection | DOAJ |
| description | The BengalDeltaFish [1] dataset resolves the common challenge of recognizing fish species in real-world fish market environments by providing a diverse, large-scale image collection of different fish species captured under uncontrolled and realistic conditions. Unlike traditional fish datasets that are captured in controlled conditions and backgrounds, this dataset’s images are collected directly from local fish markets, maintaining natural lighting variations and uncontrolled backgrounds, where fish are often placed on ice, in baskets, trays, or alongside other fish. The dataset contains 33 different fish species commonly found in local markets, including rare species that are not widely available in existing datasets. It includes 4560 annotated images that have been preprocessed and randomly split into training, validation, and testing sets for optimal use in deep learning model training, validation, and evaluation. The 98.23% mAP@50 achieved in YOLOv11s [2] training verifies the dataset’s potential for creating applications or tools that work reliably in the field. This dataset has significant reuse potential across multiple domains, including: preventing fish mislabeling to ensure consumers receive the correct species, ensuring fair pricing, and monitoring illegal sales. By reducing the gap between controlled datasets and real-world applications, BengalDeltaFish [1] serves as a valuable resource for AI-driven fish detection and classification. The dataset loading sample code for this dataset is available on GitHub with proper documentation.https://github.com/281096alif/BengalDeltaFish |
| format | Article |
| id | doaj-art-b5eb0d32d1c6401f96e7590fd7174420 |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-b5eb0d32d1c6401f96e7590fd71744202025-08-20T02:59:41ZengElsevierData in Brief2352-34092025-08-016111176410.1016/j.dib.2025.111764BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley DataSabrina Alim Dipa0Arjun Pal1Md. Shoaib Shahria2Md. Delwar Shahadat Deepu3Raiyan Rahman4Corresponding author.; United International University, BangladeshUnited International University, BangladeshUnited International University, BangladeshUnited International University, BangladeshUnited International University, BangladeshThe BengalDeltaFish [1] dataset resolves the common challenge of recognizing fish species in real-world fish market environments by providing a diverse, large-scale image collection of different fish species captured under uncontrolled and realistic conditions. Unlike traditional fish datasets that are captured in controlled conditions and backgrounds, this dataset’s images are collected directly from local fish markets, maintaining natural lighting variations and uncontrolled backgrounds, where fish are often placed on ice, in baskets, trays, or alongside other fish. The dataset contains 33 different fish species commonly found in local markets, including rare species that are not widely available in existing datasets. It includes 4560 annotated images that have been preprocessed and randomly split into training, validation, and testing sets for optimal use in deep learning model training, validation, and evaluation. The 98.23% mAP@50 achieved in YOLOv11s [2] training verifies the dataset’s potential for creating applications or tools that work reliably in the field. This dataset has significant reuse potential across multiple domains, including: preventing fish mislabeling to ensure consumers receive the correct species, ensuring fair pricing, and monitoring illegal sales. By reducing the gap between controlled datasets and real-world applications, BengalDeltaFish [1] serves as a valuable resource for AI-driven fish detection and classification. The dataset loading sample code for this dataset is available on GitHub with proper documentation.https://github.com/281096alif/BengalDeltaFishhttp://www.sciencedirect.com/science/article/pii/S2352340925004913Sustainable fishingBiodiversity researchMarket regulationMachine learningDeep learningObject detection |
| spellingShingle | Sabrina Alim Dipa Arjun Pal Md. Shoaib Shahria Md. Delwar Shahadat Deepu Raiyan Rahman BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data Data in Brief Sustainable fishing Biodiversity research Market regulation Machine learning Deep learning Object detection |
| title | BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data |
| title_full | BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data |
| title_fullStr | BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data |
| title_full_unstemmed | BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data |
| title_short | BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data |
| title_sort | bengaldeltafish a local dataset for fish detection in bangladeshi marketsmendeley data |
| topic | Sustainable fishing Biodiversity research Market regulation Machine learning Deep learning Object detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925004913 |
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