Mini Traffic Detection - Dataset Ninja

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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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.

Sample annotation mask from Mini Traffic Detection

Sample image from Mini Traffic Detection

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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.

Rows 1-8 of 8

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
car➔ any	31	82	2.65	21.88%
bus➔ any	30	74	2.47	41.67%
truck➔ any	29	60	2.07	35.33%
person➔ any	29	170	5.86	22.1%
bicycle➔ any	29	84	2.9	33.71%
traffic_light➔ any	28	140	5	16.75%
motorcycle➔ any	27	61	2.26	35.98%
stop_sign➔ 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.

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.

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.

Rows 1-10 of 725

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	person any	person28.jpg	600 x 800	322px	53.67%	100px	12.5%	3.36%
2➔	person any	person28.jpg	600 x 800	322px	53.67%	100px	12.5%	6.71%
3➔	traffic_light any	trafficlight28.jpg	600 x 800	592px	98.67%	228px	28.5%	26.44%
4➔	traffic_light any	trafficlight28.jpg	600 x 800	592px	98.67%	228px	28.5%	28.12%
5➔	traffic_light any	trafficlight24.jpg	600 x 800	439px	73.17%	173px	21.62%	15.19%
6➔	traffic_light any	trafficlight24.jpg	600 x 800	439px	73.17%	173px	21.62%	15.82%
7➔	truck any	truck30.jpg	600 x 800	308px	51.33%	611px	76.38%	27.3%
8➔	truck any	truck30.jpg	600 x 800	308px	51.33%	611px	76.38%	39.21%
9➔	bicycle any	bicycle25.jpg	600 x 800	493px	82.17%	717px	89.62%	44.23%
10➔	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 = { oct },
  note = { visited on 2024-10-31 },
}

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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