A Freshwater Fish Dataset for Visual Recognition with Manually Localized ROIs and SAM-Derived Instance Masks

The SylFishBD dataset addresses the critical need for automated identification of local fish species in Bangladesh's dynamic aquatic ecosystems and bustling fish markets. 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  × 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's most active fish-producing regions.