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Mini Traffic Detection Dataset

22681
Tagsafety
Taskinstance segmentation
Release YearMade in 2022
LicenseDbCL v1.0
Download75 MB

Introduction #

Zoltan Szekely

Mini Traffic Detection dataset comprises 8 classes with 30 instances each, divided into 70% for training and 30% for validation. Primarily designed for computer vision tasks, it focuses on traffic object detection. It’s an excellent choice for transfer learning with Detectron2 for custom object detection and segmentation projects. The dataset includes classes such as bicycle, bus, car, motorcycle, person, traffic_light, truck, and stop_sign.

Please note that bad data was detected in moto8.jpg.

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Dataset LinkHomepage

Summary #

Mini Traffic Detection is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the traffic monitoring industry. Possible applications of the dataset could be in the smart city and logistics industries.

The dataset consists of 226 images with 725 labeled objects belonging to 8 different classes including car, bus, bicycle, and other: person, truck, traffic_light, motorcycle, and stop_sign.

Images in the Mini Traffic Detection 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 (158 images) and val (68 images). The dataset was released in 2022.

Here are the visualized examples for the classes:

Explore #

Mini Traffic Detection dataset has 226 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 Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
OpenSample annotation mask from Mini Traffic DetectionSample image from Mini Traffic Detection
πŸ‘€
Have a look at 226 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 8 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 1-8 of 8
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
carβž”
any
31
82
2.65
21.88%
busβž”
any
30
74
2.47
41.67%
truckβž”
any
29
60
2.07
35.33%
personβž”
any
29
170
5.86
22.1%
bicycleβž”
any
29
84
2.9
33.71%
traffic_lightβž”
any
28
140
5
16.75%
motorcycleβž”
any
27
61
2.26
35.98%
stop_signβž”
any
26
54
2.08
8.18%

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 1-8 of 8
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
person
any
170
5.77%
26.55%
0.07%
28px
4.67%
556px
92.67%
289px
48.18%
17px
2.12%
295px
36.88%
traffic_light
any
140
6.18%
64.77%
0.04%
28px
4.67%
599px
99.83%
216px
36.01%
11px
1.38%
536px
67%
bicycle
any
84
18.19%
74.4%
0.06%
48px
8%
547px
91.17%
269px
44.84%
11px
1.38%
763px
95.38%
car
any
82
14.24%
53.4%
0.32%
32px
5.33%
391px
65.17%
170px
28.4%
61px
7.62%
772px
96.5%
bus
any
74
30.23%
76.98%
0.96%
66px
11%
596px
99.33%
317px
52.91%
56px
7%
722px
90.25%
motorcycle
any
61
24.66%
59.58%
0%
7px
1.17%
516px
86%
289px
48.11%
5px
0.62%
739px
92.38%
truck
any
60
29.82%
58.81%
3.34%
95px
15.83%
525px
87.5%
344px
57.32%
174px
21.75%
675px
84.38%
stop_sign
any
54
7.09%
18.31%
0.74%
79px
13.17%
294px
49%
188px
31.41%
56px
7%
321px
40.12%

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 725 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 725
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”
person
any
person28.jpg
600 x 800
322px
53.67%
100px
12.5%
3.36%
2βž”
person
any
person28.jpg
600 x 800
322px
53.67%
100px
12.5%
6.71%
3βž”
traffic_light
any
trafficlight28.jpg
600 x 800
592px
98.67%
228px
28.5%
26.44%
4βž”
traffic_light
any
trafficlight28.jpg
600 x 800
592px
98.67%
228px
28.5%
28.12%
5βž”
traffic_light
any
trafficlight24.jpg
600 x 800
439px
73.17%
173px
21.62%
15.19%
6βž”
traffic_light
any
trafficlight24.jpg
600 x 800
439px
73.17%
173px
21.62%
15.82%
7βž”
truck
any
truck30.jpg
600 x 800
308px
51.33%
611px
76.38%
27.3%
8βž”
truck
any
truck30.jpg
600 x 800
308px
51.33%
611px
76.38%
39.21%
9βž”
bicycle
any
bicycle25.jpg
600 x 800
493px
82.17%
717px
89.62%
44.23%
10βž”
bicycle
any
bicycle25.jpg
600 x 800
493px
82.17%
717px
89.62%
73.64%

License #

Mini Traffic Detection is under DbCL v1.0 license.

Source

Citation #

If you make use of the Mini Traffic Detection data, please cite the following reference:

@dataset{Mini Traffic Detection,
  author={Zoltan Szekely},
  title={Mini Traffic Detection},
  year={2022},
  url={https://www.kaggle.com/datasets/zoltanszekely/mini-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-mini-traffic-detection-dataset,
  title = { Visualization Tools for Mini Traffic Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/mini-traffic-detection } },
  url = { https://datasetninja.com/mini-traffic-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Mini Traffic Detection 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='Mini Traffic Detection', 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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