DeepFish (A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis)

Accepted at Nature Scientific Reports [Paper] [Dataset] [Project]

To install DeepFish as a Python package for access outside the repo:

python setup.py install OR pip install -e .

Install requirements

pip install -r requirements.txt

pip install git+https://github.com/ElementAI/LCFCN

Download

• Download the DeepFish dataset from here

1. Train and test on single image

Localization

python scripts/train_single_image.py -e loc -d ${PATH_TO_DATASET}

This outputs the following image

Segmentation

python scripts/train_single_image.py -e seg -d ${PATH_TO_DATASET}

This outputs the following image

2. Train and test on the dataset

Run the following command to reproduce the experiments in the paper:

python trainval.py -e ${TASK} -sb ${SAVEDIR_BASE} -d ${DATADIR} -r 1

The variables (${...}) can be substituted with the following values:

• TASK : loc, seg, clf, reg
• SAVEDIR_BASE: Absolute path to where results will be saved
• DATADIR: Absolute path containing the downloaded datasets

Experiment hyperparameters are defined in exp_configs.py

Citations

If you use the DeepFish dataset in your work, please cite it as:

@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={https://doi.org/10.1038/s41598-020-71639-x}
}