Datasets:
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README.md
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@@ -18,7 +18,7 @@ All the images have been annotated by six annotators to contain one of three cla
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Each of the six annotators have labelled all the images (that is, six individual annotations are provided for each image), which allows for analyzing how inter-annotator disagreement can affect the performance of machine learning models.
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Cross-validation splits and date-based splits are provided in the [jambo_splits_public.csv](jambo_splits_public.csv) file. Check out the starter notebook [howto_jambo.ipynb](howto_jambo.ipynb) to get started.
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For more information about the dataset and baseline models, please see the paper presented the ECCV 2024 Computer Vision for Ecology (CV4E) Workshop:
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[Underwater Uncertainty: A Multi-Annotator Image Dataset for Benthic Habitat
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Classification](https://vbn.aau.dk/ws/portalfiles/portal/738329849/JAMBO_ECCV_vbn.pdf) (Springer link [here](doi.org/10.1007/978-3-031-92387-6_6))
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Each of the six annotators have labelled all the images (that is, six individual annotations are provided for each image), which allows for analyzing how inter-annotator disagreement can affect the performance of machine learning models.
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Cross-validation splits and date-based splits are provided in the [jambo_splits_public.csv](jambo_splits_public.csv) file. Check out the starter notebook [howto_jambo.ipynb](howto_jambo.ipynb) to get started.
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For more information about the dataset and baseline models, please see the paper presented at the ECCV 2024 Computer Vision for Ecology (CV4E) Workshop:
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[Underwater Uncertainty: A Multi-Annotator Image Dataset for Benthic Habitat
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Classification](https://vbn.aau.dk/ws/portalfiles/portal/738329849/JAMBO_ECCV_vbn.pdf) (Springer link [here](doi.org/10.1007/978-3-031-92387-6_6))
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