The iWildCam2020-WILDS dataset is a variant of the iWildCam 2020 dataset. iWildCam2020-WILDS is a benchmark dataset designed
to test OOD generalization for the task of species classification. The label space consists of 182 species. Each domain corresponds to a different location of the camera trap. The training and test images belong to disjoint sets of locations in the OOD setting.
Variants: iWildCam2020-WILDS
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Image Classification | COSMO | Reviving the Context: Camera Trap … | 2023-12-31 |
| Image Classification | Fish | Gradient Matching for Domain Generalization | 2021-04-20 |
| Image Classification | Empirical Risk Minimization (ERM) | WILDS: A Benchmark of in-the-Wild … | 2020-12-14 |
| Image Classification | ABSGD | Attentional-Biased Stochastic Gradient Descent | 2020-12-13 |
| Image Classification | Group DRO | Does Distributionally Robust Supervised Learning … | 2016-11-07 |
| Image Classification | CORAL | Deep CORAL: Correlation Alignment for … | 2016-07-06 |
Recent papers with results on this dataset: