Overview

The tasks are based on BDD100K, the largest driving video dataset to date supporting heterogenous multi-task learning. It contains 100,000 videos representing more than 1000 hours of driving experience with more than 100 million frames. The videos come with GPU/IMU data for trajectory information. The BDD100K dataset now provides annotations of the 10 tasks: image tagging, lane detection, drivable area segmentation, object detection, semantic segmentation, instance segmentation, multi-object detection tracking, multi-object segmentation tracking, domain adaptation and imitation learning. These diverse tasks make the study of heterogenous multi-task learning possible.

For the CVPR 2020 Workshop on Autonomous Driving, we host the multi-object detection tracking challenge on CodaLab detailed below. Challenges on the other tasks will be announced on our dataset website.

Video Data
Explore 100,000 HD video sequences of over 1,100-hour driving experience across many different times in the day, weather conditions, and driving scenarios. Our video sequences also include GPS locations, IMU data, and timestamps.
Road Object Detection
2D Bounding Boxes annotated on 100,000 images for bus, traffic light, traffic sign, person, bike, truck, motor, car, train, and rider. Instance Segmentation
Explore over 10,000 diverse images with pixel-level and rich instance-level annotations.
Driveable Area
Learn complicated drivable decisions from 100,000 images.
Lane Markings
Multiple types of lane marking annotations on 100,000 images for driving guidance.

Data Format

Folder Structure

BDD100K
├── images
│   └── 100k
│       ├── test
│       │   ├── <image_name.jpg>
│       │   └── ...
│       ├── train
│       │   ├── <image_name.jpg>
│       │   └── ...
│       └── val
│           ├── <image_name.jpg>
│           └── ...
└── labels
    ├── det_20
    │   ├── det_val.json
    │   └── det_train.json
    ├── drivable
    │   └── polygons
    │           ├── drivable_train.json
    │           └── drivable_val.json
    └── lane

Label Format

Each json file contains a list of frame objects, and each frame object has the format below. The format follows the schema of BDD100K data format.

- name: string
- videoName: string
- index: int
- labels: [ ]
    - id: string
    - category: string
    - attributes:
        - Crowd: boolean
        - Occluded: boolean
        - Truncated: boolean
    - box2d:
        - x1: float
        - y1: float
        - x2: float
        - y2: float

There are 11 object categories in this release:

pedestrian
rider
other person
car
bus
truck
train
trailer
other vehicle
motorcycle
bicycle

Notes:

• The same instance shares "id" across frames.
• The "pedestrian", "bicycle", and "motorcycle" correspond to the "person", "bike", and "motor" classes in the BDD100K Detection dataset.
• We consider "other person", "trailer", and "other vehicle" as distractors, which are ignored during evaluation. We only evaluate the multi-object tracking of the other 8 categories.
• We set three super-categories: "person" (with classes "pedestrian" and"rider"), "vehicle" ("car", "bus", "truck", and "train"), and "bike" ("motorcycle" and "bicycle") for the purpose of evaluation.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{yu2020bdd100k,
  title={BDD100K: A diverse driving dataset for heterogeneous multitask learning},
  author={Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen, Yingying and
Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2636--2645},
  year={2020}
}