Pomerania Fish: A dataset for fishes across Pomerania freshwater waterbodies in-situ environments
AbstractHigh-quality datasets are crucial for computer vision in endangered fish monitoring. Species similarities and dynamic environments pose challenges. The Pomeranian region, a key salmonid refuge with ongoing restoration efforts, necessitates robust monitoring to assess restoration success. To facilitate environmentally sustainable research, we introduce a dataset, Pomerania Fish (PomerFish), comprising two sub-datasets: PomerFishObj and PomerFishSeg, each with corresponding annotations. This dataset, compiled by salmonid experts, focuses on endangered Central European salmon populations. It was collected from 2015 to 2024 in the Pomerania Region, Central Europe, using a GoPro Hero 5 camera. The dataset comprises: (1) PomerFishObj, containing 14,989 high-resolution images with manually annotated bounding boxes and 3,273 negative samples (fish absence); and (2) PomerFishSeg, containing 1,115 images, including 1,038 with polygon-based segmentation masks and 77 negative samples. Characterized by high-resolution imagery, large data volume, and comprehensive habitat records beyond species, it enables training for detailed underwater observations and precise species growth estimations. This dataset supports targeted conservation and habitat management, providing crucial resources for research, species detection, and conservation practices.
Data availability
The code, model, and associated notebooks for data processing and baseline computation are available in our Zenodo repository: https://zenodo.org/records/17432128.42. The software toolkit, developed using Python 3.10 and the PyTorch 2.3 deep learning framework, offers the following functionalities, including data preprocessing, model training, model evaluation, deployment and inference, and visualization.
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