FishWIO: a labeled image dataset of Western Indian Ocean reef fishes for training and testing classification algorithms

Abstract Monitoring fish communities is essential for understanding biodiversity dynamics and coral reef ecosystem health. Underwater imaging provides a non-invasive and repeatable approach for such monitoring, yet analysis of large volumes of video data remains extremely time-consuming for experts. Resolving such a bottleneck is today within reach, yet towards automated fish identification, large and high-quality, labelled image datasets are critical for training and testing reliable deep learning models. However, to date, no such dataset exists for the Western Indian Ocean (WIO), a global biodiversity hotspot hosting more than 300 common non-cryptobenthic fish species and facing increasing anthropogenic pressures. This paper presents a novel and publicly available dataset of 114,664 images annotated from 186 videos recorded using fixed underwater cameras on shallow reef habitats from Mayotte archipelago. All images were labelled and validated by trained marine biologists following a standardized protocol. Each image includes detailed metadata describing recording conditions. The dataset comprises 124 reef fish species (including 110 with >200 images) and 8 background classes. This dataset will allow training and testing automated fish classification models.