Deep learning models are usually developed and tested under the implicit assumption that the training and test data are drawn independently and identically distributed (IID) from the same distribution. Overlooking out-of-distribution (OOD) images can result in poor performance in unseen or adverse viewing conditions, which is common in real-world scenarios.
This competition aims to tackle typical computer vision tasks (i.e. Multi-class Classification, Object Detection, ...) on OOD images which follows a different distribution than the training images.
The dataset can be accessed at https://bzhao.me/OOD-CV
