Dataset Ninja LogoDataset Ninja:

Google Recaptcha Image Dataset

1173031676
Tagsecurity
Taskobject detection
Release YearMade in 2022
LicenseCC0 1.0
Download435 MB

Introduction #

Mike Mazurov

The Google Recaptcha V2 Image dataset is tailored for object detection and classification assignments. Comprising 11,730 images with 2,584 labeled objects falling into three distinct classes — stair, crosswalk, and chimney — this dataset features a range of 12 categories, including car, other, crosswalk, bus, hydrant, palm, traffic_light, bicycle, bridge, stair, chimney, and motorcycle. Forming a segment of Recaptcha V2 Solve, this dataset offers a diverse array of images designed to facilitate tasks in object detection and classification.

Motivation

Recaptcha V2 Solver is a Google Recaptcha V2 automated solution service. Many examples of Recaptcha V2 can be too difficult for a human to solve a captcha problem, it takes from time to time to complete it until the site makes sure that it is a real person. Authors’ service will shift the responsibility for passing the captcha to artificial intelligence, while you drink coffee and use something more important.

During the study of repeated solutions, many tools were found that solve Recaptcha V2 by sound (one of them). Much to my surprise, during the operation, authors did not find a single implementation that would solve Recaptcha V2 from pictures. Authors were presented with an interesting solution for the implementation of Recaptcha, about getting images.

Please note that the annotations are of poor quality

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Dataset LinkHomepageDataset LinkGitHub

Summary #

Google Recaptcha V2 Image is a dataset for object detection and classification tasks. Possible applications of the dataset could be in the security industry.

The dataset consists of 11730 images with 2584 labeled objects belonging to 3 different classes including stair, crosswalk, and chimney.

Images in the Google Recaptcha Image dataset have bounding box annotations. There are 11215 (96% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 12 category: car (3558 images), other (1340 images), crosswalk (1240 images), bus (1209 images), hydrant (952 images), palm (911 images), traffic_light (791 images), bicycle (780 images), bridge (533 images), stair (211 images), chimney (124 images), and motorcycle (81 images). The dataset was released in 2022.

Dataset Poster

Explore #

Google Recaptcha Image dataset has 11730 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 Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
OpenSample annotation mask from Google Recaptcha ImageSample image from Google Recaptcha Image
👀
Have a look at 11730 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 3 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 1-3 of 3
Class
Images
Objects
Count on image
average
Area on image
average
stair
rectangle
212
1513
7.14
19.17%
crosswalk
rectangle
180
889
4.94
19.77%
chimney
rectangle
124
182
1.47
11.49%

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 1-3 of 3
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
stair
rectangle
1513
4.15%
52.5%
0.08%
3px
2.5%
120px
100%
20px
16.48%
3px
2.5%
119px
99.17%
crosswalk
rectangle
889
5.14%
64.19%
0.01%
1px
0.83%
99px
82.5%
21px
17.25%
1px
0.83%
120px
100%
chimney
rectangle
182
7.84%
57.48%
0.17%
6px
5%
120px
100%
45px
37.29%
3px
2.5%
93px
77.5%

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 2584 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 2584
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
stair
rectangle
Stair (424).png
120 x 120
33px
27.5%
32px
26.67%
7.33%
2
stair
rectangle
Stair (424).png
120 x 120
24px
20%
53px
44.17%
8.83%
3
stair
rectangle
Stair (424).png
120 x 120
24px
20%
54px
45%
9%
4
crosswalk
rectangle
Cross (157).png
120 x 120
19px
15.83%
41px
34.17%
5.41%
5
crosswalk
rectangle
Cross (157).png
120 x 120
10px
8.33%
77px
64.17%
5.35%
6
crosswalk
rectangle
Cross (157).png
120 x 120
7px
5.83%
42px
35%
2.04%
7
crosswalk
rectangle
Cross (157).png
120 x 120
7px
5.83%
33px
27.5%
1.6%
8
crosswalk
rectangle
Cross (157).png
120 x 120
8px
6.67%
25px
20.83%
1.39%
9
crosswalk
rectangle
Cross (157).png
120 x 120
6px
5%
19px
15.83%
0.79%
10
crosswalk
rectangle
Cross (157).png
120 x 120
6px
5%
12px
10%
0.5%

License #

Google Recaptcha V2 Image is under CC0 1.0 license.

Source

Citation #

If you make use of the Google Recaptcha Image data, please cite the following reference:

@dataset{Google Recaptcha Image,
  author={Mike Mazurov},
  title={Google Recaptcha V2 Image},
  year={2022},
  url={https://www.kaggle.com/datasets/mikhailma/test-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-google-recaptcha-image-dataset,
  title = { Visualization Tools for Google Recaptcha Image Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/google-recaptcha-image } },
  url = { https://datasetninja.com/google-recaptcha-image },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Google Recaptcha Image 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='Google Recaptcha Image', 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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