FishNet: A Large-scale Dataset and Benchmark for Fish Recognition, Detection, and Functional Trait Prediction

Aquatic species are essential components of the world's ecosystem, and the preservation of aquatic biodiversity is crucial for maintaining proper ecosystem functioning. Unfortunately, increasing anthropogenic pressures such as overfishing, climate change, and coastal development pose significant threats to aquatic biodiversity. To address this challenge, it is necessary to design an automatic aquatic species monitoring systems that can help researchers and policymakers better understand changes in aquatic ecosystems and take appropriate actions to preserve biodiversity. However, the development of such systems is impeded by a lack of large-scale diverse aquatic species datasets. Existing aquatic species recognition datasets generally have a limited number of species, nor do they provide functional trait data, and so have only narrow potential for application. To address the need for generalized systems that can recognize, locate, and predict a wide array of species and their functional traits, we present FishNet, a large-scale diverse dataset containing 94,532 meticulously organized images from 17,357 aquatic species, organized according to aquatic biological taxonomy (order, family, genus, and species). We further build three benchmarks, i.e., fish classification, fish detection, and functional trait prediction, inspired by ecological research needs, to facilitate the development of aquatic species recognition systems, and promote further research in the field of aquatic ecology. Our FishNet dataset has the potential to encourage the development of more accurate and effective tools for the monitoring and protection of aquatic ecosystems, and hence take effective action toward the conservation of our planet's aquatic biodiversity. Our dataset and code will be released at https://fishnet-2023.github.io/.