BDD100K-weather(OOD Setting)
Introduced by Yu et al. in BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
BDD100K-weather is a dataset which is inherited from BDD100K using image attribute labels for Out-of-Distribution object detection. All images in BDD100K are categorized into six domains, including clear, overcast, foggy, partly cloudy, rainy and snowy. Clear and overcast are used for training while the rest is used for testing, moreover, per training domain is sampled 1.5k images at most while per testing domain is sampled 0.5k images at most. Thus, we have BDD100K-weather (paper is under review).
Papers
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