OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations
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
Data and resources
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Marrable, D., Tippaya, S., Barker, K., Harvey, E., Beirwagen, S., Wyatt, M., Bainbridge, S., Stowar, M. (2023) Generalised deep learning model for…HTML Open
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Marrable, D., Barker, K., Tippaya, S., Wyatt, M., Bainbridge, S., Stowar, M., & Larke, J. (2022). Accelerating Species Recognition and Labelling of Fish From…HTML Open
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Codebase used to generate fish crops from event measure filesHTML Open
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GitHub Repository - OzFish - Public dataset of Australian fish species for advancing machine learning researchHTML Open
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audio/basic Open
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License GraphicPNG Open
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Additional information
| Date (revision) | 2026-10-25 |
|---|---|
| Status | onGoing |
| Update frequency | asNeeded |
| Metadata language | en |
| License | Creative Commons Attribution 4.0 |
| Source metadata | ISO 19115 XML |
| Last updated | 2026-11-05 |
| Created | 2026-11-05 |
| API | package_show |