TartanAdversity is a synthetically augmented autonomous-driving object-detection
dataset. It takes clear-weather street scenes from
BDD100K and re-renders them under adverse
weather (fog, rain, snow) using two different augmentation pipelines, keeping the
original object annotations. It is the "fully synthetic" arm of a benchmark that
studies the learnability of synthetic training data: how well detectors trained
on synthetic weather generalize to real… See the full description on the dataset page:
https://huggingface.co/datasets/JohnYanxinLiu/TartanAdversity.
