Object Detection
70,000/10,000/20,000 images for train/val/test, 1.8M objects.
Main Features
Large-scale
100K driving videos (40 seconds each) collected from more than 50K rides, covering New York, San Francisco Bay Area, and other regions.
Diverse
Contains diverse scene types such as city streets, residential areas, and highways. Recorded in diverse weather conditions at different times of the day.
Multi-task
10 taksk in total: Lane detection, object detection, semantic segmentation, instance segmentation, multi-object tracking, segmentation tracking and more.
720p
High resolution
30fps
High frame rate
GPS/IMU
Trajectories
50k rides
Crowd sourced
Multiple Tasks
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.
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.
Leaderboard
Label Visualization
BDD100K is compatible with the labels generated by Scalabel. The labels are released in Scalabel Format.