Dataset Ninja LogoDataset Ninja:

Indian Roads Dataset

322741582
Tagself-driving, robotics
Taskinstance segmentation
Release YearMade in 2021
LicenseGNU GPL 2.0
Download1004 MB

Introduction #

Simranjeet Singh, Soofiyan Atar, Aditya Panwaret al.

The authors of the Indian Roads dataset have created it for delivery robots with knee-height delivery robots. Sidewalk delivery robots can also use this dataset as it consists of side road data as well. The dataset consists of a semantic segmentation dataset of Indian roads for roads, potholes, footpaths, shallow paths, and backgrounds. The authors of the dataset have tested it using MobileNetv3 and ESPNetv2.

Dataset LinkHomepage

Summary #

Semantic Segmentation Dataset of Indian Roads is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the automotive industry.

The dataset consists of 3227 images with 8129 labeled objects belonging to 4 different classes including road, footpath, shallow, and other: pothole.

Images in the Indian Roads 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. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (2475 images) and val (752 images). The dataset was released in 2021 by the e-Yantra, IIT Bombay, India.

Dataset Poster

Explore #

Indian Roads dataset has 3227 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 Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
OpenSample annotation mask from Indian RoadsSample image from Indian Roads
πŸ‘€
Have a look at 3227 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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 1-4 of 4
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
roadβž”
polygon
3224
3438
1.07
62.32%
footpathβž”
polygon
2060
2905
1.41
9.49%
shallowβž”
polygon
645
1516
2.35
6.32%
potholeβž”
polygon
230
270
1.17
2.88%

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 1-4 of 4
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
road
polygon
3438
58.29%
94.99%
0.01%
9px
1.25%
720px
100%
585px
81.2%
23px
1.8%
1280px
100%
footpath
polygon
2905
6.67%
69.84%
0.01%
8px
1.11%
720px
100%
333px
46.18%
15px
1.17%
1280px
100%
shallow
polygon
1516
2.66%
71.24%
0.05%
10px
1.39%
681px
94.58%
106px
14.77%
25px
1.95%
1279px
99.92%
pothole
polygon
270
2.42%
64.12%
0.05%
23px
3.19%
672px
93.33%
241px
33.46%
40px
3.12%
1280px
100%

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 8129 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 8129
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”
road
polygon
2_006_frame0406_leftImg8bit.jpg
720 x 1280
719px
99.86%
1087px
84.92%
67.94%
2βž”
footpath
polygon
2_006_frame0406_leftImg8bit.jpg
720 x 1280
717px
99.58%
525px
41.02%
25.64%
3βž”
footpath
polygon
2_006_frame0406_leftImg8bit.jpg
720 x 1280
144px
20%
358px
27.97%
1.59%
4βž”
pothole
polygon
2_006_frame0406_leftImg8bit.jpg
720 x 1280
34px
4.72%
76px
5.94%
0.22%
5βž”
road
polygon
1_017_frame1029_leftImg8bit.jpg
720 x 1280
663px
92.08%
1280px
100%
71.76%
6βž”
road
polygon
1_017_frame0217_leftImg8bit.jpg
720 x 1280
688px
95.56%
1279px
99.92%
71.65%
7βž”
road
polygon
2_006_frame1715_leftImg8bit.jpg
720 x 1280
694px
96.39%
1268px
99.06%
81.63%
8βž”
road
polygon
1_007_frame0420_leftImg8bit.jpg
720 x 1280
561px
77.92%
1263px
98.67%
51.12%
9βž”
footpath
polygon
1_007_frame0420_leftImg8bit.jpg
720 x 1280
244px
33.89%
571px
44.61%
3.97%
10βž”
shallow
polygon
1_007_frame0420_leftImg8bit.jpg
720 x 1280
76px
10.56%
186px
14.53%
1.21%

License #

Semantic Segmentation Dataset of Indian Roads is under GNU GPL 2.0 license.

Source

Citation #

If you make use of the Indian Roads data, please cite the following reference:

@dataset{Indian Roads,
  author={Simranjeet Singh and Soofiyan Atar and Aditya Panwar and Amit Kumar and Srijan Agrawal and Sravya G and Ravikumar Chaurasia and Shreyas Sule and Kavi Arya},
  title={Semantic Segmentation Dataset of Indian Roads},
  year={2021},
  url={https://www.kaggle.com/datasets/eyantraiit/semantic-segmentation-datasets-of-indian-roads}
}

Source

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

@misc{ visualization-tools-for-indian-roads-semantic-segmentation-dataset,
  title = { Visualization Tools for Indian Roads Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/indian-roads-semantic-segmentation } },
  url = { https://datasetninja.com/indian-roads-semantic-segmentation },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Indian Roads 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='Indian Roads', 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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