NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation
This dataset supports conservation research by providing annotated images for field-deployable systems in extreme conditions, but it is incremental as it applies existing methods to new data.
The authors introduced NDD20, a large-scale dataset of dolphin images for coarse and fine-grained segmentation, addressing the lack of open-source datasets in conservation research, and reported baseline results using standard deep learning architectures.
We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation. This dataset, the first release of the NDD, was created in response to the rapid expansion of computer vision into conservation research and the production of field-deployable systems suited to extreme environmental conditions -- an area with few open source datasets. NDD20 contains a large collection of above and below water images of two different dolphin species for traditional coarse and fine-grained segmentation. All data contained in NDD20 was obtained via manual collection in the North Sea around the Northumberland coastline, UK. We present experimentation using standard deep learning network architecture trained using NDD20 and report baselines results.