BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

• DOI: 10.48550/arxiv.1805.04687
• Publication Date: 2018-01-01
• Authors: Fisher Yu; Haofeng Chen; Xin Wang; Wenqi Xian; Yingying Chen; Fangchen Liu; Vashisht Madhavan; Trevor Darrell
• Keywords: Benchmark (surveying); Training set

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 usually constrained a small set problems on one dataset, while real-world computer applications require performing various complexities. We construct BDD100K, the largest video dataset with 100K videos 10 evaluate exciting progress image recognition algorithms The possesses geographic, environmental, weather diversity, which is useful training models that less likely be surprised by new conditions. Based this diverse we build benchmark heterogeneous how solve together. Our experiments show special strategies needed perform such tasks. BDD100K opens door future studies important venue.