DeepFish

A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. About 40 thousand underwater images (1920 × 1080) from 20 habitats in the marine environments of tropical Australia, with classification, point-level (localization/counting) and segmentation labels.

• Paper: Scientific Reports, 2020
• Code: https://github.com/alzayats/DeepFish
• Project page: https://alzayats.github.io/DeepFish/
• Original data record: James Cook University, https://doi.org/10.25903/5f617fb6d6e0e

This repository is a mirror of the original DeepFish.tar for faster downloads worldwide. The file is byte-identical to the one on the original data portal.

Download

pip install -U huggingface_hub
hf download Alzayats/DeepFish DeepFish.tar DeepFish.tar.sha256 --repo-type dataset --local-dir .
sha256sum -c DeepFish.tar.sha256
tar xf DeepFish.tar

Interrupted downloads resume when you run the command again.

File	Size	SHA-256
DeepFish.tar	7.1 GiB (7,621,627,392 bytes)	8acabb8a314fa8eb45fa9cc6829c84bdc127b1f8f605f2e152304e98b6dbb46c

Structure

DeepFish/
    Classification/   # fish / no-fish labels, per habitat
    Localization/     # point-level annotations
    Segmentation/     # segmentation masks

License

CC BY 4.0. Data owners: Michael Bradley, James Cook University.

Citation

@article{saleh2020realistic,
  title={A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis},
  author={Saleh, Alzayat and Laradji, Issam H and Konovalov, Dmitry A and Bradley, Michael and Vazquez, David and Sheaves, Marcus},
  journal={Scientific Reports},
  volume={10},
  number={1},
  pages={14671},
  year={2020},
  publisher={Nature Publishing Group UK London},
  doi={10.1038/s41598-020-71639-x}
}