
# Dataset Card for BasicShapes1000

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://eezy.com


### Dataset Summary

This is a synthetic dataset containing randomly-generated SVGs with various shapes
### Supported Tasks and Leaderboards

NA

### Languages

NA

## Dataset Structure

The dataset is composed of 4 base domains, plus a 'mixed' domain that is a superset of the other 4:
* `circles` - only circles
* `squares` - only squares
* `squares_and_circles` - circles and squares present in the same svg
* `scer` - squares, circles, ellipses, and rectangles present in the same svg
* `mixed` - an aggregation of all of the above

### Data Instances

There's stuff there

### Data Fields

Each example has 4 fields:

* `svg` - the raw svg as a string
* `png` - a raster rendering of the svg with a white background
* `object_mask` - a black/white mask that defines the outlines of the svg objects
* `layer_mask` - a greyscale mask that defines layers of svg objects - overlap regions are brighter. Created by making all the objects white and semi-transparent
* `segments` - a numpy array in the shape `(N,512,512), dtype='bool'` where N is the number of svg objects. The array is a mask of each object with `True` in the area of the object

### Data Splits

Train & validation include the layer and object masks, test does not

## Dataset Creation

Generated by randomly inserting objects into an SVG.

### Curation Rationale

Objects should have at least 50% of their bounding box visible - i.e. no big circle completely obscuring a little circle

### Source Data

`/dev/urandom`

#### Initial Data Collection and Normalization

NA

#### Who are the source language producers?

NA

### Annotations

see [Data Fields](#data-fields)

#### Annotation process

see [Data Fields](#data-fields)

#### Who are the annotators?

Imagemagick/pysvg

### Personal and Sensitive Information

Unlikely

## Considerations for Using the Data

Please do not use for world domination.

### Social Impact of Dataset

NA

### Discussion of Biases

Dataset is highly biased against triangles and concave shapes

### Other Known Limitations

Color selection is pretty limited.

## Additional Information

### Dataset Curators

[Aleks Clark](https://github.com/aleksclark)

### Licensing Information

CC-BY

### Citation Information

Link it I guess?

### Contributions

Thanks to [@aleksclark](https://github.com/aleksclark) for adding this dataset.

region:us
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TAGS
#region-us 


# Dataset Card for BasicShapes1000

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: URL


### Dataset Summary

This is a synthetic dataset containing randomly-generated SVGs with various shapes
### Supported Tasks and Leaderboards

NA

### Languages

NA

## Dataset Structure

The dataset is composed of 4 base domains, plus a 'mixed' domain that is a superset of the other 4:
* 'circles' - only circles
* 'squares' - only squares
* 'squares_and_circles' - circles and squares present in the same svg
* 'scer' - squares, circles, ellipses, and rectangles present in the same svg
* 'mixed' - an aggregation of all of the above

### Data Instances

There's stuff there

### Data Fields

Each example has 4 fields:

* 'svg' - the raw svg as a string
* 'png' - a raster rendering of the svg with a white background
* 'object_mask' - a black/white mask that defines the outlines of the svg objects
* 'layer_mask' - a greyscale mask that defines layers of svg objects - overlap regions are brighter. Created by making all the objects white and semi-transparent
* 'segments' - a numpy array in the shape '(N,512,512), dtype='bool'' where N is the number of svg objects. The array is a mask of each object with 'True' in the area of the object

### Data Splits

Train & validation include the layer and object masks, test does not

## Dataset Creation

Generated by randomly inserting objects into an SVG.

### Curation Rationale

Objects should have at least 50% of their bounding box visible - i.e. no big circle completely obscuring a little circle

### Source Data

'/dev/urandom'

#### Initial Data Collection and Normalization

NA

#### Who are the source language producers?

NA

### Annotations

see Data Fields

#### Annotation process

see Data Fields

#### Who are the annotators?

Imagemagick/pysvg

### Personal and Sensitive Information

Unlikely

## Considerations for Using the Data

Please do not use for world domination.

### Social Impact of Dataset

NA

### Discussion of Biases

Dataset is highly biased against triangles and concave shapes

### Other Known Limitations

Color selection is pretty limited.

## Additional Information

### Dataset Curators

Aleks Clark

### Licensing Information

CC-BY



Link it I guess?

### Contributions

Thanks to @aleksclark for adding this dataset.

# Dataset Card for BasicShapes1000 ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL ### Dataset Summary

This is a synthetic dataset containing randomly-generated SVGs with various shapes ### Supported Tasks and Leaderboards

NA ### Languages

NA ## Dataset Structure

The dataset is composed of 4 base domains, plus a 'mixed' domain that is a superset of the other 4:
* 'circles' - only circles
* 'squares' - only squares
* 'squares_and_circles' - circles and squares present in the same svg
* 'scer' - squares, circles, ellipses, and rectangles present in the same svg
* 'mixed' - an aggregation of all of the above ### Data Instances

There's stuff there ### Data Fields

Each example has 4 fields:

* 'svg' - the raw svg as a string
* 'png' - a raster rendering of the svg with a white background
* 'object_mask' - a black/white mask that defines the outlines of the svg objects
* 'layer_mask' - a greyscale mask that defines layers of svg objects - overlap regions are brighter. Created by making all the objects white and semi-transparent
* 'segments' - a numpy array in the shape '(N,512,512), dtype='bool'' where N is the number of svg objects. The array is a mask of each object with 'True' in the area of the object ### Data Splits

Train & validation include the layer and object masks, test does not ## Dataset Creation

Generated by randomly inserting objects into an SVG. ### Curation Rationale

Objects should have at least 50% of their bounding box visible - i.e. no big circle completely obscuring a little circle ### Source Data

'/dev/urandom' #### Initial Data Collection and Normalization

NA #### Who are the source language producers?

NA ### Annotations

see Data Fields #### Annotation process

see Data Fields #### Who are the annotators?

Imagemagick/pysvg ### Personal and Sensitive Information

Unlikely ## Considerations for Using the Data

Please do not use for world domination. ### Social Impact of Dataset

NA ### Discussion of Biases

Dataset is highly biased against triangles and concave shapes ### Other Known Limitations

Color selection is pretty limited. ## Additional Information ### Dataset Curators

Aleks Clark ### Licensing Information

CC-BY



Link it I guess? ### Contributions

Thanks to @aleksclark for adding this dataset.
TAGS
#region-us 
 # Dataset Card for BasicShapes1000 ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL ### Dataset Summary

This is a synthetic dataset containing randomly-generated SVGs with various shapes ### Supported Tasks and Leaderboards

NA ### Languages

NA ## Dataset Structure

The dataset is composed of 4 base domains, plus a 'mixed' domain that is a superset of the other 4:
* 'circles' - only circles
* 'squares' - only squares
* 'squares_and_circles' - circles and squares present in the same svg
* 'scer' - squares, circles, ellipses, and rectangles present in the same svg
* 'mixed' - an aggregation of all of the above ### Data Instances

There's stuff there ### Data Fields

Each example has 4 fields:

* 'svg' - the raw svg as a string
* 'png' - a raster rendering of the svg with a white background
* 'object_mask' - a black/white mask that defines the outlines of the svg objects
* 'layer_mask' - a greyscale mask that defines layers of svg objects - overlap regions are brighter. Created by making all the objects white and semi-transparent
* 'segments' - a numpy array in the shape '(N,512,512), dtype='bool'' where N is the number of svg objects. The array is a mask of each object with 'True' in the area of the object ### Data Splits

Train & validation include the layer and object masks, test does not ## Dataset Creation

Generated by randomly inserting objects into an SVG. ### Curation Rationale

Objects should have at least 50% of their bounding box visible - i.e. no big circle completely obscuring a little circle ### Source Data

'/dev/urandom' #### Initial Data Collection and Normalization

NA #### Who are the source language producers?

NA ### Annotations

see Data Fields #### Annotation process

see Data Fields #### Who are the annotators?

Imagemagick/pysvg ### Personal and Sensitive Information

Unlikely ## Considerations for Using the Data

Please do not use for world domination. ### Social Impact of Dataset

NA ### Discussion of Biases

Dataset is highly biased against triangles and concave shapes ### Other Known Limitations

Color selection is pretty limited. ## Additional Information ### Dataset Curators

Aleks Clark ### Licensing Information

CC-BY



Link it I guess? ### Contributions

Thanks to @aleksclark for adding this dataset.