CVPR 2022 BDD100K Challenges
We are hosting multi-object tracking (MOT) and segmentation (MOTS) challenges based on BDD100K, the largest open driving video dataset as part of the CVPR 2022 Workshop on Autonomous Driving (WAD).
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News
2021-10: We released our model zoo and leaderboards!
2021-12: We released the pose estimation annotations and models!
2022-03: We released our CVPR 2022 challenges!
Challenges
Highlights
Large-scale
100K driving videos collected from more than 50K rides. Each video is 40-second long and 30fps. More than 100 million frames in total.
Diverse
Diverse scene types including city streets, residential areas, and highways, and diverse weather conditions at different times of the day.
Multi-task
Lane detection, object detection, semantic segmentation, instance segmentation, panoptic segmentation, multi-object tracking, segmentation tracking and more.
720p
High resolution
30fps
High frame rate
GPS/IMU
Trajectories
50k rides
Crowd sourced
Multiple Tasks
Object Detection
70,000/10,000/20,000 images for train/val/test, 1.8M objects.
Instance Segmentation
7,000/1,000/2,000 images for train/val/test, 120K masks.
Multi Object Tracking
1,400/200/400 videos for train/val/test, 160K instances, 4M objects.
Segmentation Tracking
154/32/37 videos for train/val/test, 25K instances, 480K masks.
Semantic Segmentation
7,000/1,000/2,000 images for train/val/test, 40 object classes.
Lane Marking
70,000/10,000/20,000 images for train/val/test, 8 main categories.
Drivable Area
70,000/10,000/20,000 images for train/val/test, 8 main categories.
Image Tagging
6 weather conditions, 6 scene types, 3 distinct times of the day
Imitation Learning
GPS/IMU recordings with visual input and the driving trajectories.
Domain Adaptation
Diverse weather, road and daytime conditions.
Label Visualization
Docs & Tools
We provide documents and tools for inspection, preparation, and evaluation of the BDD100K dataset.
Data Download
You can simply log in and download the data in your browser after agreeing to BDD100K license.
Visualization
We provides scripts to parse and visualize the labels, and a tool to display the trajectories.
Label Format
We use a consistent data annotation format across all different tasks. We choose the Scalabel [link] format for this.
Evaluation
We provide evaluation scripts, online testing servers and challenges to verify your algorithm.
BDD100K is compatible with the labels generated by Scalabel. The labels are released in Scalabel Format.