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The development of foundation vision models has pushed the general visualrecognition to a high level, but cannot well address the fine-grainedrecognition in specialized domain such as invasive species classification.Identifying and managing invasive species has strong social and ecologicalvalue. Currently, most invasive species datasets are limited in scale and covera narrow range of species, which restricts the development of deep-learningbased invasion biometrics systems. To fill the gap of this area, we introducedSpecies196, a large-scale semi-supervised dataset of 196-category invasivespecies. It collects over 19K images with expert-level accurate annotationsSpecies196-L, and 1.2M unlabeled images of invasive species Species196-U. Thedataset provides four experimental settings for benchmarking the existingmodels and algorithms, namely, supervised learning, semi-supervised learning,self-supervised pretraining and zero-shot inference ability of largemulti-modal models. To facilitate future research on these four learningparadigms, we conduct an empirical study of the representative methods on theintroduced dataset. The dataset is publicly available athttps://species-dataset.github.io/.

Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition