6db7f1a3-c931-4743-b5f2-fcbd95cbed71 urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-06-23T09:55:39 creation ISO 19115 2016 OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations 6db7f1a3-c931-4743-b5f2-fcbd95cbed71 This dataset has been developed as part of the Australian Research Data Commons Data Discoveries program (https://ardc.edu.au/project/machine-learning-dataset-creation-for-australian-fish-species-from-baited-remote-underwater-videos-bruv/), with the aim to futher advance research into machine learning for the automated detection of fish from video. The dataset was generated from over 3000 videos which were historically analysed with the Event Measure software package and sourced from the Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University of Technology. The dataset is comprised of the following: - ~80k labelled crops of fish extracted from the videos, from over 500 species, 200 genera and 70 families - ~45k bounding box annotations (suitable for YOLO,RetinaNet) of fish/no fish across 1800 frames {"type": "Polygon", "coordinates": [[[112.93945312500001, -24.126701958681682], [137.54882812500003, -24.126701958681682], [137.54882812500003, -10.228437266155943], [112.93945312500001, -10.228437266155943], [112.93945312500001, -24.126701958681682]]]} 112.93945312500001 137.54882812500003 -24.126701958681682 -10.228437266155943 Marrable, D., Tippaya, S., Barker, K., Harvey, E., Beirwagen, S., Wyatt, M., Bainbridge, S., Stowar, M. (2023) Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS. Frontiers in Marine Science. 10. https://doi.org/10.3389/fmars.2023.1171625 documentDigital Marrable, D., Tippaya, S., Barker, K., Harvey, E., Beirwagen, S., Wyatt, M., Bainbridge, S., Stowar, M. (2023) Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS. Frontiers in Marine Science. 10. https://doi.org/10.3389/fmars.2023.1171625 HTML Marrable, D., Barker, K., Tippaya, S., Wyatt, M., Bainbridge, S., Stowar, M., & Larke, J. (2022). Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning. Frontiers in Marine Science, 9. https://www.frontiersin.org/articles/10.3389/fmars.2022.944582 documentDigital Marrable, D., Barker, K., Tippaya, S., Wyatt, M., Bainbridge, S., Stowar, M., & Larke, J. (2022). Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning. Frontiers in Marine Science, 9. https://www.frontiersin.org/articles/10.3389/fmars.2022.944582 HTML Codebase used to generate fish crops from event measure files documentDigital Codebase used to generate fish crops from event measure files HTML GitHub Repository - OzFish - Public dataset of Australian fish species for advancing machine learning research documentDigital GitHub Repository - OzFish - Public dataset of Australian fish species for advancing machine learning research HTML AIMS Web Site documentDigital audio/basic AIMS Web Site documentDigital audio/basic AIMS Web Site documentDigital audio/basic Unnamed resource documentDigital License Graphic PNG Unnamed resource documentDigital HTML