BDD100K Blog Update

We are excited by the interest and excitement generated by our BDD100K dataset. Our data release and blog post were covered in an unsolicited article by the UC Berkeley newspaper, the Daily Cal, which was then picked up by other news services without our prompting or intervention. The paper describing this dataset is under review at the ECCV 2018 conference, and we followed the rules of that conference (as communicated to us by the Program Chairs in prompt email response when we asked for clarification following the reporter’s request; the ECCV PC’s replied that ECCV follows CVPR’s long-standing policy). We thus declined to speak to the reporters after they reached out to us. We did not, and have not, communicated with any media outlets regarding this story.

While the Daily Cal article was accurate; unfortunately, other media outlets who followed in reporting the story made claims that were attributed to us incorrectly, and which do not represent our view. In particular, several media outlets attributed to us a claim that the BDD100K dataset was “800 times” bigger than other industrial datasets, specifically mentioning Baidu’s ApolloScape. While it is true our dataset does contain more raw images than other datasets, including Baidu’s, the stated claim is misleading and we did not put that line or anything like it in a paper, blog post, or spoken comment to anyone. It appears that some reporters(s) viewed the data in tables in our paper and came up with this conclusory comment themselves as it made an exciting headline, yet attributed it to us. In fact, it is inappropriate in our view to summarize the difference between our dataset and Baidu’s in a single comment that ours is 800x larger. Comparing the number of raw images directly is not the most appropriate way to compare these types of datasets.

Importantly, different datasets focus on different aspects of the autonomous driving challenge. Our dataset is crowd-sourced, and covers a very large area and diverse visual phenomena (indeed significantly more diverse than previous efforts, in our view), but it is very clearly limited to monocular RGB image data and associated mobile device metadata. Other dataset collection efforts are complementary in our view. Baidu’s, KITTI, and CityScapes each contain important additional sensing modalities and are collected with fully calibrated apparatus including actuation channels. (The dataset from Mapillary is also notable, and similar to ours in being diverse, crowd-sourced, and densely annotated, but differs in that we include video and dynamic metadata relevant to driving control.) We look forward to projects at Berkeley and elsewhere that leverage both BDD100K and these other datasets as the research community brings the potential of autonomous driving to reality.

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