1 code implementation • 18 Dec 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
This makes it difficult to train supervised deep learning models on large and diverse datasets, which can limit the model's performance.
no code implementations • 23 Aug 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
In the third stage, the refined labels are used to train a segmentation network.
no code implementations • 11 Jun 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts.
no code implementations • 11 Jun 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
Our proposed model is trained on videos -- without any annotations -- to perform fish segmentation in underwater videos taken in situ in the wild.
no code implementations • 14 Mar 2022 • Alzayat Saleh, Marcus Sheaves, Mostafa Rahimi Azghadi
This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment.
1 code implementation • 28 Aug 2020 • Alzayat Saleh, Issam H. Laradji, Dmitry A. Konovalov, Michael Bradley, David Vazquez, Marcus Sheaves
The dataset consists of approximately 40 thousand images collected underwater from 20 \green{habitats in the} marine-environments of tropical Australia.
no code implementations • 26 May 2019 • Dmitry A. Konovalov, Alzayat Saleh, Michael Bradley, Mangalam Sankupellay, Simone Marini, Marcus Sheaves
Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier.