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Related Experiment Video

Updated: Jun 19, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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Published on: March 6, 2014

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

Shakib Absar1, Iftekhar Ahmed1, Md Istiaque Khalique1

  • 1Interdisciplinary Computer Science (InteX) Research Lab, Sylhet, 3100, Bangladesh.

Scientific Data
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

The SylFishBD dataset provides 9,075 real-world images of 9 fish species in Bangladesh, enabling automated identification and freshness assessment in markets. This benchmark supports computer vision for aquaculture and trade transparency.

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Area of Science:

  • Computer Vision
  • Aquaculture Technology
  • Data Science

Background:

  • Automated fish species identification is crucial for Bangladesh's aquaculture and markets.
  • Existing datasets lack real-world complexity, limiting model deployment.

Purpose of the Study:

  • Introduce the SylFishBD dataset for automated fish identification in Bangladesh.
  • Provide a benchmark dataset reflecting authentic market conditions.

Main Methods:

  • Collected 9,075 high-resolution images of 9 freshwater fish species over 7 months.
  • Captured images in uncontrolled market settings with diverse lighting and backgrounds.
  • Annotated images with bounding boxes and segmentation masks using the Segment Anything Model (SAM).

Main Results:

  • The SylFishBD dataset features 9,075 standardized 500x500px images.
  • Images represent 9 prevalent freshwater species under realistic market conditions.
  • Dataset includes precise annotations for computer vision tasks.

Conclusions:

  • SylFishBD bridges the gap between controlled datasets and real-world applications.
  • The dataset facilitates the development of deployable models for aquaculture, trade, and regulation.
  • Enables advancements in automated species recognition and freshness assessment.