BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
This dataset addresses the problem of limited visual content and task variety for researchers in autonomous driving, though it is incremental as it builds upon existing dataset efforts.
The authors tackled the lack of diverse and comprehensive driving datasets for multitask learning in autonomous driving by constructing BDD100K, a dataset with 100K videos and 10 tasks, which enables training models that are more robust to varied conditions.
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.