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BDD100K MOTS2020
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update dataset overview and ba...
b22ff5f·
Feb 10, 2022 7:44 AM
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Overview

The BDD100K MOTS2020 is a dataset with multi-object tracking and segmentation tasks, which contains serial instance masks tracking different instances. It is a subset of MOT 2020 Images.

DataFormat

Folder Structure

BDD100K_MOTS2020
├── images
│   └── seg_track_20
│       ├── test
│       │   ├── cabc30fc-e7726578
│       │   │   ├── cabc30fc-e7726578-0000001.jpg
│       │   │   └── ...
│       │   └── ...
│       ├── train
│       └── val
└── labels
    └── seg_track_20
        ├── bitmasks
        │   ├── train
        │   │   ├── 000d4f89-3bcbe37a
        │   │   │   ├── 000d4f89-3bcbe37a-0000001.png
        │   │   │   └── ...
        │   │   └── ...
        │   └── val
        └── polygons
            ├── train
            │   ├── 000d4f89-3bcbe37a.json
            │   └── ...
            └── val

Label Format

We provide labels in both JSON and bitmask formats. In JSON the masks are stored as poly2ds. instance masks for each image are stored in an RGBA png file. For the RGBA image, the first byte, R, is used for the category id range from 1 (0 is used for the background). Moreover, G is for the instance attributes. Currently, four attributes are used, they are “truncated”, “occluded”, “crowd” and “ignore”. Note that boxes with “crowd” or “ignore” labels will not be considered during testing. The above four attributes are stored in least significant bits of G. Given this, G = (truncated << 3) + (occluded << 2) + (crowd << 1) + ignore. Finally, the B channel and A channel store the instance id, which can be computed as (B << 8) + A.

Citation

@InProceedings{bdd100k, author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen, Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor}, title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020} }

Data Preview
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🎉Many thanks to Hello Dataset for contributing the dataset
Basic Information
Application ScenariosAutonomous Driving
AnnotationsMultiPolygon TrackingSemanticMaskInstanceMask
TasksObject Tracking
LicenseCustom
Updated on2022-02-11 08:13:06
Metadata
Data TypeImage
Data Volume44,704
Annotation Amount37,220
File Size5.35GB
Copyright Owner
UC Berkeley
Annotator
Unknown