  The development of foundation vision models has pushed the general visual
recognition to a high level, but cannot well address the fine-grained
recognition in specialized domain such as invasive species classification.
Identifying and managing invasive species has strong social and ecological
value. Currently, most invasive species datasets are limited in scale and cover
a narrow range of species, which restricts the development of deep-learning
based invasion biometrics systems. To fill the gap of this area, we introduced
Species196, a large-scale semi-supervised dataset of 196-category invasive
species. It collects over 19K images with expert-level accurate annotations
Species196-L, and 1.2M unlabeled images of invasive species Species196-U. The
dataset provides four experimental settings for benchmarking the existing
models and algorithms, namely, supervised learning, semi-supervised learning,
self-supervised pretraining and zero-shot inference ability of large
multi-modal models. To facilitate future research on these four learning
paradigms, we conduct an empirical study of the representative methods on the
introduced dataset. The dataset is publicly available at
https://species-dataset.github.io/.
