  Aiming at facilitating a real-world, ever-evolving and scalable autonomous
driving system, we present a large-scale dataset for standardizing the
evaluation of different self-supervised and semi-supervised approaches by
learning from raw data, which is the first and largest dataset to date.
Existing autonomous driving systems heavily rely on `perfect' visual perception
models (i.e., detection) trained using extensive annotated data to ensure
safety. However, it is unrealistic to elaborately label instances of all
scenarios and circumstances (i.e., night, extreme weather, cities) when
deploying a robust autonomous driving system. Motivated by recent advances of
self-supervised and semi-supervised learning, a promising direction is to learn
a robust detection model by collaboratively exploiting large-scale unlabeled
data and few labeled data. Existing datasets either provide only a small amount
of data or covers limited domains with full annotation, hindering the
exploration of large-scale pre-trained models. Here, we release a Large-Scale
2D Self/semi-supervised Object Detection dataset for Autonomous driving, named
as SODA10M, containing 10 million unlabeled images and 20K images labeled with
6 representative object categories. To improve diversity, the images are
collected within 27833 driving hours under different weather conditions,
periods and location scenes of 32 different cities. We provide extensive
experiments and deep analyses of existing popular self/semi-supervised
approaches, and give some interesting findings in autonomous driving scope.
Experiments show that SODA10M can serve as a promising pre-training dataset for
different self-supervised learning methods, which gives superior performance
when fine-tuning with different downstream tasks (i.e., detection,
semantic/instance segmentation) in autonomous driving domain. More information
can refer to https://soda-2d.github.io.
