Dataset Ninja LogoDataset Ninja:

Dhaka-AI Dataset

3953211704
Tagsurveillance, energy-and-utilities
Taskobject detection
Release YearMade in 2020
LicenseCC0 1.0
Download2 GB

Introduction #

ASM Shihavuddin, Mohammad Rifat Ahmmad Rashid

The creators of the Dhaka-AI: Dhaka Traffic Detection Challenge Dataset emphasize the distinctive traffic conditions in Dhaka city. Although it’s a city of significant size, only 7% of its roads meet urban standards. However, it contends with a staggering 8 million daily commuters, all within a 306 square kilometer area. In response to this complex challenge, they introduced the AI-based Dhaka Traffic Detection Challenge.

The primary objective of this challenge is to evaluate the effectiveness of advanced techniques in automated traffic detection using AI and ICT solutions, especially in the context of a diverse urban environment like Dhaka. Furthermore, the challenge seeks to encourage collaboration among experts in the region and establish a thriving AI-based community in South-East Asia.

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Dataset LinkHomepageDataset LinkKaggle

Summary #

Dhaka-AI: Dhaka Traffic Detection Challenge Dataset is a dataset for an object detection task. It is used in the urban planning research. Possible applications of the dataset could be in the vehicle detection domain.

The dataset consists of 3953 images with 24318 labeled objects belonging to 21 different classes including car, bus, motorbike, and other: three wheelers (CNG), rickshaw, truck, pickup, minivan, suv, van, bicycle, auto rickshaw, human hauler, wheelbarrow, ambulance, minibus, taxi, army vehicle, scooter, policecar, and garbagevan.

Images in the Dhaka-AI dataset have bounding box annotations. There are 956 (24% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (3003 images), test (500 images), and test2 (450 images). The dataset was released in 2020.

Dataset Poster

Explore #

Dhaka-AI dataset has 3953 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 Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
OpenSample annotation mask from Dhaka-AISample image from Dhaka-AI
πŸ‘€
Have a look at 3953 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 21 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 21
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
carβž”
rectangle
1618
5467
3.38
5.47%
busβž”
rectangle
1554
3327
2.14
10.37%
motorbikeβž”
rectangle
1184
2280
1.93
4.23%
three wheelers (CNG)βž”
rectangle
1167
2986
2.56
3.78%
rickshawβž”
rectangle
1019
3536
3.47
8.3%
truckβž”
rectangle
842
1492
1.77
10.49%
pickupβž”
rectangle
792
1224
1.55
5.67%
minivanβž”
rectangle
575
934
1.62
3.33%
suvβž”
rectangle
536
857
1.6
3.06%
vanβž”
rectangle
448
755
1.69
3.24%

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 21
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
car
rectangle
5467
1.71%
53.59%
0%
7px
0.67%
2093px
75.56%
99px
10.15%
7px
0.42%
2539px
96.67%
rickshaw
rectangle
3536
2.57%
77.87%
0.01%
7px
0.57%
2675px
98.61%
158px
15.87%
7px
0.48%
1914px
99.06%
bus
rectangle
3327
4.91%
94.66%
0.01%
8px
0.77%
2641px
99.91%
191px
20.18%
9px
0.62%
3295px
99.95%
three wheelers (CNG)
rectangle
2986
1.57%
44.19%
0%
8px
0.64%
2047px
76.39%
122px
11.48%
5px
0.32%
1542px
76.35%
motorbike
rectangle
2280
2.22%
84.25%
0%
8px
0.5%
1568px
89.12%
131px
11.37%
4px
0.24%
1917px
99.84%
truck
rectangle
1492
6%
98.84%
0.01%
7px
1.05%
3086px
99.83%
215px
22.52%
10px
0.72%
3735px
99.58%
pickup
rectangle
1224
3.71%
83.89%
0.01%
8px
1.06%
2187px
99.69%
148px
17.73%
10px
0.5%
3692px
95.82%
minivan
rectangle
934
2.06%
47.62%
0.01%
11px
0.69%
894px
82.13%
110px
11.79%
12px
0.75%
1368px
71.25%
suv
rectangle
857
1.92%
75.32%
0.01%
11px
0.64%
1205px
98.98%
133px
11.11%
11px
0.67%
1558px
82.55%
van
rectangle
755
1.95%
54.1%
0.01%
8px
0.93%
1251px
80.37%
117px
11.54%
10px
0.6%
1518px
79.06%

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 24318 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 24318
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”
motorbike
rectangle
Navid_239.jpg
1080 x 1920
157px
14.54%
115px
5.99%
0.87%
2βž”
motorbike
rectangle
Navid_239.jpg
1080 x 1920
86px
7.96%
91px
4.74%
0.38%
3βž”
motorbike
rectangle
Navid_239.jpg
1080 x 1920
70px
6.48%
54px
2.81%
0.18%
4βž”
motorbike
rectangle
Navid_239.jpg
1080 x 1920
86px
7.96%
60px
3.12%
0.25%
5βž”
bicycle
rectangle
Navid_239.jpg
1080 x 1920
126px
11.67%
117px
6.09%
0.71%
6βž”
car
rectangle
Navid_239.jpg
1080 x 1920
132px
12.22%
150px
7.81%
0.95%
7βž”
car
rectangle
Navid_239.jpg
1080 x 1920
93px
8.61%
185px
9.64%
0.83%
8βž”
suv
rectangle
Navid_239.jpg
1080 x 1920
108px
10%
173px
9.01%
0.9%
9βž”
truck
rectangle
Pias (382).jpg
720 x 1280
237px
32.92%
251px
19.61%
6.45%
10βž”
bus
rectangle
Navid_292.jpg
720 x 1280
211px
29.31%
188px
14.69%
4.3%

License #

Dhaka Traffic Detection Challenge Dataset is under GNU GPL 2.0 license.

Citation #

If you make use of the Dhaka-AI data, please cite the following reference:

@dataset{Dhaka-AI,
  author={ASM Shihavuddin and Mohammad Rifat Ahmmad Rashid},
  title={Dhaka Traffic Detection Challenge Dataset},
  year={2020},
  url={https://www.kaggle.com/datasets/rifat963/dhakaai-dhaka-based-traffic-detection-dataset}
}

Source

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

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

Download #

Dataset Dhaka-AI 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='Dhaka-AI', 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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