Data Download

The BDD100K data and annotations can be obtained at https://bdd-data.berkeley.edu/. You can simply log in and download the data in your browser after agreeing to BDD100K license. On the downloading portal, you will see a list of downloading buttons with the name corresponding to the subsections on this page. The files behind the buttons are described below.

Videos

100K video clips

Size

1.8TB

md5

253d9a2f9d89d2b09d8d93f397aecdd7

Video Torrent

Torrent for the 100K video clips

Video Parts

The 100K videos broken into 100 parts for easy downloading.

Info

The GPS/IMU information recorded along with the videos

Size

3.9GB

md5

043811ff34b2fca6d50f37d263a65c93

100K Images

The images in this package are the frames at the 10th second in the videos. The split of train, validation, and test sets are the same with the whole video set. They are used for object detection, drivable area, lane marking.

Size

5.3GB

md5

5a0359c86a0b8713adab1eee9a3041cb

- bdd100k
    - images
        - 100k
            - train
            - val
            - test

10K Images

There are 10K images in this package for for semantic segmentation, instance segmentation and panoptic segmentation. Due to some legacy reasons, not all the images here have corresponding videos. So it is not a subset of the 100K images, even though there is a significant overlap.

Size

1.1GB

md5

08f26aecceda982568063d3d5873378e

- bdd100k
    - images
        - 10k
            - train
            - val
            - test

Labels

Annotations of road object detection in JSON format released in 2018. The video attributes, including weather, scene, and timeofday, are also stored in the downloaded json files. We revised the detection annotations in 2020 and released them as Detection 2020 Labels in the list. You are recommended to use the new labels. This detection annotation set is kept for comparison with legacy results.

Size

107MB

md5

e21be3e7d6a07ee439faf61e769667e4

Drivable Area

Masks, colormaps and original json files for drivable area. The mask format is explained at: Semantic Segmentation Format.

Size

466MB

md5

98dcfa4c3c68e2e86f132ac085f8e329

- bdd100k
    - labels
        - drivable
            - masks
                - train
                - val
            - colormaps
                - train
                - val
            - polygons
                - drivable_train.json
                - drivable_val.json

Lane Marking

Masks, colormaps and original json files for lane marking. The mask format is explained at: Lane Marking Format.

Size

434MB

md5

80d3d5daf57b9de340d564f0c4b395ea

- bdd100k
    - labels
        - lane
            - masks
                - train
                - val
            - colormaps
                - train
                - val
            - polygons
                - lane_train.json
                - lane_val.json

Semantic Segmentation

Masks, colormaps and original json files for semantic segmentation. The mask format is explained at: Semantic Segmentation Format.

Size

331MB

md5

098c0c17ca58364c47c5882b3eb7058d

- bdd100k
    - labels
        - sem_seg
            - masks
                - train
                - val
            - colormaps
                - train
                - val
            - polygons
                - sem_seg_train.json
                - sem_seg_val.json

Instance Segmentation

Bitmasks, colormaps and original json files for instance segmentation. The bitmask format is explained at: Instance Segmentation Format.

Size

98MB

md5

4254b7674b827ebf970c06745eb07fe9

- bdd100k
    - labels
        - ins_seg
            - bitmasks
                - train
                - val
            - colormaps
                - train
                - val
            - polygons
                - ins_seg_train.json
                - ins_seg_val.json

Panoptic Segmentation

Bitmasks, colormaps and original json files for panoptic segmentation. The bitmask format is explained at: Panoptic Segmentation Format.

Size

363MB

md5

fc37642ae024ffb223182ef01238d007

- bdd100k
    - labels
        - pan_seg
            - bitmasks
                - train
                - val
            - colormaps
                - train
                - val
            - polygons
                - pan_seg_train.json
                - pan_seg_val.json

MOT 2020 Labels

Multi-object bounding box tracking training and validation labels released in 2020. This is a subset of the 100K videos, but the videos are resampled to 5Hz from 30Hz. The labels are in Scalabel Format. The same object in each video has the same label id but objects across videos are always distinct even if they have the same id.

Size

115MB

md5

6be40e0ca56a83ddeba2ed6bff50f9e6

- bdd100k
    - labels
        - box_track_20
            - train
            - val

MOT 2020 Images

Multi-object bounding box tracking videos in frames released in 2020. The videos are a subset of the 100K videos, but they are resampled to 5Hz from 30Hz.

- bdd100k
    - images
        - track
            - train
            - val
            - test

Detection 2020 Labels

Multi-object detection validation and testing labels released in 2020. This is for the same set of images in the previous key frame annotation. However, this annotation went through the additional quality check. The original detection set is deprecated.

Size

53MB

md5

b86a3e1b7edbcad421b7dad2b3987c94

- bdd100k
    - labels
        - det_20
            - det_train.json
            - det_val.json

MOTS 2020 Labels

Multi-object tracking and segmentation training and validation labels released in 2020 The bitmask format is explained at: Instance Segmentation Format.

Size

418MB

md5

c29fb3fc54b119c8e5d980ce74d7b8b6

- bdd100k
    - labels
        - seg_track_20
            - bitmasks
                - train
                - val
            - colormaps
                - train
                - val
            - polygons
                - train
                - val

MOTS 2020 Images

Multi-object tracking and segmentation videos in frames released in 2020. This is a subset of MOT 2020 Images.

Size

5.4GB

md5

7c52a52f3c9cc880c91b264870a1d4bb

- bdd100k
    - images
        - seg_track_20
            - train
            - val
            - test