Dataset
Tools to Apply Deep Convolution Neural Networks to Predict Species in Great Lakes Region
This project contains training and evaluation scripts as well as trained models and generated results for several deep convolutional neural networks (DCNNs). The DCNNs predicted the species of a fish in an image from one of 13 different species of fish from the Great Lakes region. Full details on the evaluation of the classifiers are available in Eickholt et al. 2020. Training and evaluation data are available at FishL Low Resolution Images of Fish from the Great Lakes Region, DOI 10.17605/OSF.IO/KQVG8. Eickholt, J., Kelly, D., Bryan, J., Miehls, S. and Zielinski, D., 2020. Advancements towards selective barrier passage by automatic species identification: applications of deep convolutional neural networks on images of dewatered fish. ICES Journal of Marine Science, 77(7-8), pp.2804-2813. https://doi.org/10.1093/icesjms/fsaa150.