The Self Driving Cars dataset comprises images and associated labeled semantic segmentations obtained using the CARLA self-driving car simulator. This dataset was created within the context of the Lyft Udacity Challenge. It serves as valuable data for training machine learning algorithms to recognize semantic segmentation of objects like cars, roads, and more.

Semantic Segmentation for Self Driving Cars is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the automotive industry.

The dataset consists of 5000 images with 344334 labeled objects belonging to 13 different classes including sky, road, tree, and other: sidewalk, car, pole, road markings, building, street infrastructure, fence, wall, traffic, and pedestrian.

Images in the Self Driving Cars dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2018.

Here is the visualized example grid with animated annotations:

Semantic Segmentation for Self Driving Cars is under CC0 1.0 license.

Source

If you make use of the Self Driving Cars data, please cite the following reference:

@dataset{Self Driving Cars,
	author={Kumaresan Manickavelu},
	title={Semantic Segmentation for Self Driving Cars},
	year={2018},
	url={https://www.kaggle.com/datasets/kumaresanmanickavelu/lyft-udacity-challenge}
}

Source

Dataset Self Driving Cars 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='Self Driving Cars', 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.