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Self Driving Cars Dataset

5000132137
Tagself-driving
Tasksemantic segmentation
Release YearMade in 2018
LicenseCC0 1.0
Download3 GB

Introduction #

Kumaresan Manickavelu

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.

Dataset LinkHomepage

Summary #

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:

Explore #

Self Driving Cars dataset has 5000 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
OpenSample annotation mask from Self Driving CarsSample image from Self Driving Cars
πŸ‘€
Have a look at 5000 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 13 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 13
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
treeβž”
mask
5000
61336
12.27
12.3%
skyβž”
mask
5000
110147
22.03
30.76%
sidewalkβž”
mask
5000
25405
5.08
7.86%
roadβž”
mask
5000
14683
2.94
19.44%
carβž”
mask
5000
15718
3.14
16.63%
poleβž”
mask
4909
18461
3.76
0.48%
road markingsβž”
mask
4884
9133
1.87
0.76%
buildingβž”
mask
4501
32651
7.25
9.36%
street infrastructureβž”
mask
4490
23867
5.32
1.12%
fenceβž”
mask
4353
15040
3.46
1%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 13
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
sky
mask
110147
1.4%
46.3%
0.01%
1px
0.17%
392px
65.33%
36px
5.96%
1px
0.12%
800px
100%
tree
mask
61336
1%
35.9%
0.01%
2px
0.33%
506px
84.33%
58px
9.74%
1px
0.12%
800px
100%
building
mask
32651
1.29%
26.65%
0.01%
1px
0.17%
438px
73%
58px
9.71%
1px
0.12%
662px
82.75%
sidewalk
mask
25405
1.55%
16.12%
0.01%
1px
0.17%
229px
38.17%
61px
10.14%
2px
0.25%
800px
100%
street infrastructure
Unknown
23867
0.21%
9.83%
0.01%
1px
0.17%
442px
73.67%
29px
4.86%
2px
0.25%
387px
48.38%
pole
mask
18461
0.13%
4.76%
0.01%
3px
0.5%
519px
86.5%
87px
14.49%
1px
0.12%
114px
14.25%
car
mask
15718
5.29%
21.17%
0.01%
3px
0.5%
343px
57.17%
59px
9.86%
3px
0.38%
800px
100%
fence
mask
15040
0.29%
10.13%
0.01%
2px
0.33%
428px
71.33%
44px
7.3%
1px
0.12%
800px
100%
road
mask
14683
6.62%
29.03%
0.01%
1px
0.17%
240px
40%
137px
22.84%
3px
0.38%
800px
100%
wall
mask
13800
0.25%
4.34%
0.01%
2px
0.33%
134px
22.33%
18px
3.01%
2px
0.25%
371px
46.38%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 344334 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 344334
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
sky
mask
F5-74.png
600 x 800
297px
49.5%
800px
100%
36.68%
2βž”
sky
mask
F5-74.png
600 x 800
26px
4.33%
14px
1.75%
0.04%
3βž”
sky
mask
F5-74.png
600 x 800
14px
2.33%
21px
2.62%
0.03%
4βž”
sky
mask
F5-74.png
600 x 800
20px
3.33%
23px
2.88%
0.04%
5βž”
sky
mask
F5-74.png
600 x 800
44px
7.33%
26px
3.25%
0.11%
6βž”
sky
mask
F5-74.png
600 x 800
13px
2.17%
9px
1.12%
0.01%
7βž”
sky
mask
F5-74.png
600 x 800
16px
2.67%
13px
1.62%
0.02%
8βž”
sky
mask
F5-74.png
600 x 800
7px
1.17%
12px
1.5%
0.01%
9βž”
sky
mask
F5-74.png
600 x 800
17px
2.83%
8px
1%
0.02%
10βž”
sky
mask
F5-74.png
600 x 800
18px
3%
22px
2.75%
0.04%

License #

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

Source

Citation #

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

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-self-driving-cars-dataset,
  title = { Visualization Tools for Self Driving Cars Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/self-driving-cars } },
  url = { https://datasetninja.com/self-driving-cars },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

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.

. . .

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