We propose MammalNet, a large-scale video benchmark for recognizing mammals and their behavior. It is built around a biological mammal taxonomy spanning 17 orders, 69 families and 173 mammal categories, and includes 12 common high-level mammal behaviors (e.g., hunt, groom).
MammalNet enables the study of animal and behavior recognition, both separately and jointly. It also facilitates investigating challenging compositional scenarios which test models' zero- and low-shot transfer abilities.
Moreover, MammalNet includes behavior detection by localizing when a behavior occurs in an untrimmed video. Our dataset is the first to enable animal behavior analysis at scale in an ecologically-grounded manner, and exemplifies multiple challenges for the computer vision community, such as recognition of imbalanced, hierarchical distributions of fine-grained categories and generalization to unseen or seldom seen scenarios.
Download
The MammamNet dataset for both challenge tracks will be made available soon.
Q&A
If you encounter any technical issue related to the dataset, or if you're missing critical information, please open a ticket on our GitHub repository.
Licenses
We only provide the annotations and do not distribute the videos. The licenses for our annotations are as follows: CC BY license.