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It comprises 9,075 high-resolution images of 9 prevalent freshwater species, captured under uncontrolled, real-world conditions over 7 months to capture seasonal variations in appearance, freshness, and market presentation. Unlike prior datasets limited to controlled studio settings, SylFishBD faithfully replicates the complexity of operational fish markets: diverse lighting (natural daylight to fluorescent), varied viewpoints (top-down, angled, close-up), cluttered backgrounds (ice, trays, water, scales), and natural occlusions (vendor hands, overlapping fish). Each image contains exactly one clearly centered fish instance, annotated with a tight bounding box and a high-precision binary segmentation mask generated using the Segment Anything Model (SAM). All images are standardized to 500 \u00a0\u00d7 500 pixels, organized hierarchically by species, and accompanied by comprehensive metadata, enabling seamless integration into machine learning pipelines. The dataset supports a wide range of computer vision tasks, including classification, object detection, instance segmentation, and morphological analysis, without requiring additional preprocessing. By bridging the gap between laboratory-based datasets and authentic market environments, SylFishBD serves as a robust, publicly available benchmark for developing deployable models in real-world aquaculture, trade transparency, price monitoring, and regulatory oversight. It empowers researchers and practitioners to advance automated species recognition, freshness assessment, and fair market practices in one of the world\u2019s most active fish-producing regions.<\/jats:p>","DOI":"10.1038\/s41597-026-07552-1","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T12:14:09Z","timestamp":1781698449000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Freshwater Fish Dataset for Visual Recognition with Manually Localized ROIs and SAM-Derived Instance Masks"],"prefix":"10.1038","volume":"13","author":[{"given":"Shakib","family":"Absar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iftekhar","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Istiaque","family":"Khalique","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Shorfuzzaman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Mahfuzur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asrar U.","family":"Haque","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Saidur Rahman","family":"Kohinoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"7552_CR1","doi-asserted-by":"publisher","first-page":"111764","DOI":"10.1016\/j.dib.2025.111764","volume":"61","author":"SA Dipa","year":"2025","unstructured":"Dipa, S. A., Pal, A., Shahria, M. S., Deepu, M. D. S. & Rahman, R. BengalDeltaFish: A local dataset for fish detection in Bangladeshi markets. Data in Brief 61, 111764 (2025).","journal-title":"Data in Brief"},{"issue":"11","key":"7552_CR2","first-page":"e0287891","volume":"19","author":"PK Das","year":"2024","unstructured":"Das, P. K., Hasan, S., Faisal, S., Ahmed, Z. U. & Kabir, M. R. BD-freshwater-fish: An image dataset from Bangladesh for AI-powered automatic fish species classification and detection toward smart aquaculture. PLoS ONE 19(11), e0287891 (2024).","journal-title":"PLoS ONE"},{"key":"7552_CR3","doi-asserted-by":"crossref","unstructured":"Ataullha, M., Jisun, S. P., Jahan, I. & Rahman, M. BanFish: A Dataset for Classifying Common Bangladeshi Fish Species, in Proc. 2024 6th International Conference on Electrical, Electronic and Information Communication Technology (ICEEICT), pp. 1-6. (2024). [Online]. Available: https:\/\/ieeexplore.ieee.org\/document\/10534462.","DOI":"10.1109\/ICEEICT62016.2024.10534462"},{"key":"7552_CR4","doi-asserted-by":"publisher","unstructured":"Rahman, M. W. et al. BDFreshFish: A comprehensive image dataset of Bangladeshi freshwater fishes to develop AI-driven solutions for autonomous fisheries management. In Proceedings of the 3rd International Conference on Computing Advancements (pp. 816-821). Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3723178.3723286 (2025).","DOI":"10.1145\/3723178.3723286"},{"key":"7552_CR5","doi-asserted-by":"publisher","first-page":"104565","DOI":"10.1016\/j.dib.2019.104565","volume":"27","author":"SZH Shah","year":"2019","unstructured":"Shah, S. Z. H. Fish species dataset from Pakistan for visual features-based classification. Data in Brief 27, 104565 (2019).","journal-title":"Data in Brief"},{"key":"7552_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01416-0","volume":"9","author":"R Gonzalez","year":"2022","unstructured":"Gonzalez, R. et al. The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation. Scientific Data 9, 287 (2022).","journal-title":"Scientific Data"},{"issue":"1","key":"7552_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-04211-1","volume":"12","author":"G Ridgway","year":"2025","unstructured":"Ridgway, G. et al. A global dataset of freshwater fish trophic interactions. Scientific Data 12(1), 52 (2025).","journal-title":"Scientific Data"},{"key":"7552_CR8","doi-asserted-by":"crossref","unstructured":"Kirillov, A. et al. Segment Anything. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4015\u20134026 (2023).","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"2","key":"7552_CR9","first-page":"1","volume":"39","author":"Y Chen","year":"2025","unstructured":"Chen, Y., Son, M., Hua, C. & Kim, J.-Y. AoP-SAM: Automation of prompts for efficient segmentation. Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI) 39(2), 1\u20139 (2025).","journal-title":"Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI)"},{"key":"7552_CR10","unstructured":"Lian, S. et al. Diving into underwater: Segment anything model guided underwater salient instance segmentation and a large-scale dataset. Proceedings of Machine Learning Research (PMLR), vol. 235, Art. no. 1190, (2024)."},{"key":"7552_CR11","doi-asserted-by":"publisher","unstructured":"Absar, S., Ahmed, I. & Kohinoor, M. S. R. SylFishBD: A Freshwater Fish Dataset. Kaggle. https:\/\/doi.org\/10.34740\/kaggle\/ds\/8761062 (2025).","DOI":"10.34740\/kaggle\/ds\/8761062"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07552-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07552-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07552-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T23:00:26Z","timestamp":1791154826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07552-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,17]]},"references-count":11,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["7552"],"URL":"https:\/\/doi.org\/10.1038\/s41597-026-07552-1","relation":{},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,17]]},"assertion":[{"value":"17 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"1383"}}