A Diverse Driving Dataset for Heterogeneous Multitask Learning

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News

2023-03: We released our CVPR 2023 challenges!
2022-07: We released our ECCV 2022 challenges!
2022-03: We released our CVPR 2022 challenges!
2021-12: We released the pose estimation annotations and models!
2021-10: We released our model zoo and leaderboards!

Challenges

CVPR 2023 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 2023 Workshop on Autonomous Driving (WAD).

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ECCV 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 ECCV 2022 Self-supervised Learning for Next-Generation Industry-level Autonomous Driving (SSLAD) Workshop.

Read more

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).

Read more

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.

BDD100K

Facilitate algorithmic study on large-scale diverse visual data and multiple tasks

Download
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

Object Detection

Object Detection

Object Detection

Object Detection

Instance Segmentation

Instance Segmentation

Instance Segmentation

Instance Segmentation

Instance Segmentation

Instance Segmentation

Semantic Seg

Semantic Seg

Semantic Seg

Semantic Seg

Semantic Seg

Semantic Seg

Drivable Area

Drivable Area

Drivable Area

Drivable Area

Drivable Area

Drivable Area

Box Tracking

Box Tracking

Box Tracking

Box Tracking

Box Tracking

Box Tracking

Seg Tracking

Seg Tracking

Seg Tracking

Seg Tracking

Seg Tracking

Seg Tracking

Pose

Pose

Pose

Pose

Pose

Pose

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.

Scalabel

BDD100K is compatible with the labels generated by Scalabel. The labels are released in Scalabel Format.

BDD100K Model Zoo

We provide popular models for each task in the BDD100K dataset

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Leaderboard

Object Detection

open end

  • 14,000/2,000/4,000 images
  • 1.8M objects
Participate

Semantic Segmentation

open end

  • 7,000/1,000/2,000 images
  • 19 classes
Participate

Multiple Object Tracking

open end

  • 1,400/200/400 videos
  • 160K instances
Participate

Instance Segmentation

open end

  • 7,000/1,000/2,000 images
  • 120K objects
Participate

Drivable Area

open end

  • 70,000/10,000/20,000 images
  • 100K images
Participate

Segmentation Tracking

open end

  • 154/32/37 videos
  • 25K instances
Participate

Pose Estimation

open end

  • 10,000/1,500/2,500 images
  • 27K instances
Participate