{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bdd100k-a-diverse-driving-video-database-with","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","arxiv_id":"1805.04687","date":"2018-05-12","proceeding":"CVPR 2020 6","authors":["Fisher Yu","Haofeng Chen","Xin Wang","Wenqi Xian","Yingying Chen","Fangchen Liu","Vashisht Madhavan","Trevor Darrell"],"abstract":"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.","url_abs":"https://arxiv.org/abs/1805.04687v2","url_pdf":"https://arxiv.org/pdf/1805.04687v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bdd100k-a-diverse-driving-video-database-with","repo_url":"https://github.com/bdd100k/bdd100k","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"bdd100k-a-diverse-driving-video-database-with","repo_url":"https://github.com/SysCV/bdd100k-models","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"bdd100k-a-diverse-driving-video-database-with","repo_url":"https://github.com/irh-ca-team-car/attention-data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"bdd100k-a-diverse-driving-video-database-with","repo_url":"https://github.com/map-learning/tl2la","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"drivable-area-detection","task_name":"Drivable Area Detection"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"lane-detection","task_name":"Lane Detection"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"multi-object-tracking-and-segmentation","task_name":"Multi-Object Tracking and Segmentation"},{"task_slug":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-instance-segmentation","task_name":"Semi-Supervised Instance Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"bdd100k","name":"BDD100K","full_name":""},{"slug":"bdd100k-weather-ood-setting","name":"BDD100K-weather(OOD Setting)","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/multiple-object-tracking-on-bdd100k-test-1","task":"Multiple Object Tracking","dataset":"BDD100K test","model":"Yu et al.","rank_in_archive_order":5,"of":5,"metrics":{"mIDF1":"44.7","mMOTA":"26.3 "},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1805.04687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04687"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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