Berkeley Deep Drive Dataset (BDD100K): A Diverse Driving Dataset for Heterogeneous Multitask Learning (Images 100K) is a dataset for instance segmentation, semantic segmentation, object detection, and identification tasks. It is used in the automotive industry.

The dataset consists of 100000 images with 2221128 labeled objects belonging to 12 different classes including car, drivable area, lane, and other: traffic sign, traffic light, person, truck, bus, bike, rider, motor, and train.

Images in the BDD100K: Images 100K dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. There are 20137 (20% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (70000 images), test (20000 images), and val (10000 images). Additionally, every image contains tags with weather, scene and timeofday, while objects contain dictionary with useful meta-information about their attributes (area type, occlusion, truncation, etc.). The dataset was released in 2020 by the UC Berkeley, USA, Cornell University, USA, UC San Diego, USA, and Element, Inc.

Here is the visualized example grid with animated annotations:

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Source

If you make use of the BDD100K Images 100K data, please cite the following reference:

@dataset{BDD100K Images 100K,
  author={Fisher Yu and Haofeng Chen and Xin Wang and Wenqi Xian and Yingying Chen and Fangchen Liu and Vashisht Madhavan and Trevor Darrell},
  title={BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning (Images 100K)},
  year={2020},
  url={https://www.bdd100k.com/}
}

Source

Dataset BDD100K: Images 100K can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='BDD100K: Images 100K', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.