Dataset Card for better-ppq

This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into your Argilla server as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.

Using this dataset with Argilla

To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade and then use the following code:

import argilla as rg

ds = rg.Dataset.from_hub("alecccdd/better-ppq", settings="auto")

This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.

Using this dataset with datasets

To load the records of this dataset with datasets, you'll just need to install datasets as pip install datasets --upgrade and then use the following code:

from datasets import load_dataset

ds = load_dataset("alecccdd/better-ppq")

This will only load the records of the dataset, but not the Argilla settings.

Dataset Structure

This dataset repo contains:

• Dataset records in a format compatible with HuggingFace datasets. These records will be loaded automatically when using rg.Dataset.from_hub and can be loaded independently using the datasets library via load_dataset.
• The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
• A dataset configuration folder conforming to the Argilla dataset format in .argilla.

The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.

Fields

The fields are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.

Field Name	Title	Type	Required
image	image	image	True

Questions

The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.

Question Name	Title	Type	Required	Description	Values/Labels
face	Face Visibility	label_selection	True	OOF: Not part of the image at all
hidden: Face would be visible, but was blured, pixelated, or otherwise obscured
partially covered: Face is partially covered by an object (e.g., hand, sunglasses, phone, emoji, mask)
partially (angle): Face is partially visible due to angle (e.g., turned away, side profile)
clear: Face is fully visible and unobstructed (clear frontal / 3-quarter view)	['OOF', 'hidden', 'partially covered', 'partially (angle)', 'clear']
eyes_visible	Both Eyes Visible?	label_selection	True	Both eyes must be clearly visible (not closed, covered, or out of frame), No in case of sunglasses or doubt	['No', 'Yes']
facial_expression_readable	Facial Expression Readable?	label_selection	True	N/A	['No', 'Unclear', 'Yes']
body_coverage_in_frame	Body Coverage in Frame	label_selection	True	No body: No person at all depicted (e.g. just a room, a banner, an object, an animal)
Partial body: Body is cut off before shoulders or knees or in an awkward position hiding some parts
Full body: Body is visible from at least one knee to at least one shoulder (face/feet not required) and no substantial parts are covered by an awkward position or objects	['No body', 'Partial body', 'Full body']
bust_size_perceivable	Bust Size Perceivable?	label_selection	True	N/A	['No', 'Yes', 'Very Well']
waist_circumference_visible	Waist Circumference Visible?	label_selection	True	No: Waist is not visible at all
Partially obscured: Waist circumference is visible but belly is hidden/turned away/covered by loose clothing
Yes: Waist circumference is clearly visible and unobstructed (either naked or tight clothing)	['No', 'Partially obscured', 'Yes']
booty_shape_visible	Booty Shape Visible?	label_selection	True	N/A	['No', 'Yes', 'Very Well']
leg_length_shape_visible	Leg Length Shape Visible?	label_selection	True	No: Legs are not visible at all
Thigh only: At least one thigh is visible clearly allowing to determine circumference/shape
Full leg: At least one full leg is visible clearly	['No', 'Thigh only', 'Full leg']
skin_tone_rendering	How realistic is skin tone?	label_selection	True	N/A	['Poor', 'Accurate']
image_noise	Image Noise Level	label_selection	True	N/A	['High', 'Moderate', 'Low/None']

Metadata

The metadata is a dictionary that can be used to provide additional information about the dataset record.

Metadata Name	Title	Type	Values	Visible for Annotators
search_group	search_group	integer	-	True

Vectors

The vectors contain a vector representation of the record that can be used in search.

Vector Name	Title	Dimensions
pe_core	pe_core	[1, 1024]

Data Splits

The dataset contains a single split, which is train.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation guidelines

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

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Citation Information

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Contributions

[More Information Needed]