/DeepFish

JCU DeepFish dataset

Primary LanguagePythonMIT LicenseMIT

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

Accepted at Nature Scientific Reports [Paper]

CNN counting Segmentation

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

CNN

Segmentation

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

This outputs the following image

CNN

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={Alzayat Saleh and Issam H. Laradji and Dmitry A. Konovalov and Michael Bradley and David Vazquez and Marcus Sheaves},
    year={2020},
    eprint={2008.12603},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}