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ItalianSigns Dataset

36111
Tagself-driving
Taskobject detection
Release YearMade in 2022
LicenseGNU GPL 3.0
Download91 MB

Introduction #

Daniel Rossi, Riccardo Salami

ItalianSigns is a dataset that includes images of road contexts captured using a smartphone camera with a resolution of 1280x720 (HD). All the images were taken in the Reggio Emilia province of Italy. This dataset consists of 362 labeled images, and each image showcases a road sign, specifically those related to speed limits. It also includes annotations for the speed limit number and a bounding box outlining the Region of Interest (ROI) containing the sign.

To locate and extract the bounding boxes around the signs, the authors utilized the HoughCircles method. In addition, they employed a K-nearest neighbors (KNN) algorithm to analyze feature vectors extracted using SIFT between the ground truth images and the regions of interest in the inference images.

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Dataset LinkHomepage

Summary #

ItalianSigns is a dataset for an object detection task. Possible applications of the dataset could be in the automotive industry.

The dataset consists of 361 images with 361 labeled objects belonging to 1 single class (sign).

Images in the ItalianSigns dataset have bounding box 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 2022.

Dataset Poster

Explore #

ItalianSigns dataset has 361 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 ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
OpenSample annotation mask from ItalianSignsSample image from ItalianSigns
πŸ‘€
Have a look at 361 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 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 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
signβž”
rectangle
361
361
1
0.57%

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 1-1 of 1
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
sign
rectangle
361
0.57%
1.85%
0.1%
31px
4.31%
126px
17.5%
69px
9.55%
31px
2.42%
135px
10.55%

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 361 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 361
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”
sign
rectangle
frame_20_09_2022__22_23_50.jpg
720 x 1280
102px
14.17%
101px
7.89%
1.12%
2βž”
sign
rectangle
frame_20_09_2022__10_30_04.jpg
720 x 1280
87px
12.08%
94px
7.34%
0.89%
3βž”
sign
rectangle
12561.jpg
720 x 1280
45px
6.25%
45px
3.52%
0.22%
4βž”
sign
rectangle
16636.jpg
720 x 1280
53px
7.36%
53px
4.14%
0.3%
5βž”
sign
rectangle
frame_20_09_2022__22_26_44.jpg
720 x 1280
71px
9.86%
71px
5.55%
0.55%
6βž”
sign
rectangle
frame_21_09_2022__15_24_02.jpg
720 x 1280
116px
16.11%
119px
9.3%
1.5%
7βž”
sign
rectangle
3161.jpg
720 x 1280
63px
8.75%
63px
4.92%
0.43%
8βž”
sign
rectangle
frame_21_09_2022__16_00_51.jpg
720 x 1280
38px
5.28%
38px
2.97%
0.16%
9βž”
sign
rectangle
frame_20_09_2022__10_21_33.jpg
720 x 1280
59px
8.19%
59px
4.61%
0.38%
10βž”
sign
rectangle
frame_20_09_2022__22_09_51.jpg
720 x 1280
93px
12.92%
93px
7.27%
0.94%

License #

ItalianSigns is under GNU GPL 3.0 license.

Citation #

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

@dataset{ItalianSigns,
  author={Daniel Rossi and Riccardo Salami},
  title={ItalianSigns},
  year={2022},
  url={https://www.kaggle.com/datasets/officialprojecto/italiansigns}
}

Source

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

@misc{ visualization-tools-for-italian-signs-dataset,
  title = { Visualization Tools for ItalianSigns Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/italian-signs } },
  url = { https://datasetninja.com/italian-signs },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset ItalianSigns 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='ItalianSigns', 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.

. . .

Disclaimer #

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