| | --- |
| | tags: |
| | - rlfh |
| | - argilla |
| | - human-feedback |
| | --- |
| | |
| | # Dataset Card for profile-picture-quality |
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| | This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). |
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| | ## Using this dataset with Argilla |
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| | To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: |
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| | ```python |
| | import argilla as rg |
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| | ds = rg.Dataset.from_hub("putazon/profile-picture-quality", settings="auto") |
| | ``` |
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| | This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation. |
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| | ## Using this dataset with `datasets` |
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| | 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: |
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| | ```python |
| | from datasets import load_dataset |
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| | ds = load_dataset("putazon/profile-picture-quality") |
| | ``` |
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| | This will only load the records of the dataset, but not the Argilla settings. |
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| | ## Dataset Structure |
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| | This dataset repo contains: |
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| | * 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](#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`. |
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| | The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. |
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| | ### Fields |
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| | 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. |
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| | | Field Name | Title | Type | Required | |
| | | ---------- | ----- | ---- | -------- | |
| | | image | image | image | True | |
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| | ### Questions |
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| | 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. |
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| | | Question Name | Title | Type | Required | Description | Values/Labels | |
| | | ------------- | ----- | ---- | -------- | ----------- | ------------- | |
| | | face_in_frame | Face in Frame | label_selection | True | Is the face captured in the frame? | ['No', 'Partially', 'Fully'] | |
| | | face_clarity | Face Clarity | label_selection | True | Quality and angle of face visibility. | ['Blurred', 'Awkward angle', 'Clear frontal/3-quarter'] | |
| | | eyes_visible | Eyes Visible | label_selection | True | Are both eyes clearly visible? | ['No', 'Partially', 'Yes'] | |
| | | facial_expression_readable | Facial Expression Readable | label_selection | True | Can the facial expression be clearly interpreted? | ['No', 'Unclear', 'Yes'] | |
| | | body_coverage_in_frame | Body Coverage in Frame | label_selection | True | How much of the body is captured? | ['Headshot only', 'Partial body', 'Full body'] | |
| | | bust_size_perceivable | Bust Size Perceivable | label_selection | True | Yes=shape assessable, Very Well=Clearly depicted | ['No', 'Yes', 'Very Well'] | |
| | | waist_circumference_visible | Waist Circumference Visible | label_selection | True | Can waist circumference be assessed? | ['No', 'Partially obscured', 'Yes'] | |
| | | booty_shape_visible | Booty Shape Visible | label_selection | True | Can booty shape be assessed? | ['No', 'Yes', 'Very Well'] | |
| | | leg_length_shape_visible | Leg Length/Shape Visible | label_selection | True | Can leg length and shape be assessed? | ['No', 'Obscured', 'Yes'] | |
| | | skin_tone_rendering | Skin Tone Rendering / Sharpness / Lighting | label_selection | True | Color accuracy of skin tones. | ['Poor', 'Acceptable', 'Accurate'] | |
| | | image_noise | Image Noise / Blurriness / Graininess | label_selection | True | Graininess or digital noise level. | ['High', 'Moderate', 'Low/None'] | |
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| | <!-- check length of metadata properties --> |
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| | ### Data Splits |
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| | The dataset contains a single split, which is `train`. |
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| | ## Dataset Creation |
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| | ### Curation Rationale |
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| | [More Information Needed] |
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| | ### Source Data |
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| | #### Initial Data Collection and Normalization |
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| | [More Information Needed] |
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| | #### Who are the source language producers? |
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| | [More Information Needed] |
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| | ### Annotations |
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| | #### Annotation guidelines |
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| | [More Information Needed] |
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| | #### Annotation process |
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| | [More Information Needed] |
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| | #### Who are the annotators? |
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| | [More Information Needed] |
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| | ### Personal and Sensitive Information |
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| | [More Information Needed] |
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| | ## Considerations for Using the Data |
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| | ### Social Impact of Dataset |
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| | [More Information Needed] |
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| | ### Discussion of Biases |
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| | [More Information Needed] |
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| | ### Other Known Limitations |
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| | [More Information Needed] |
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| | ## Additional Information |
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| | ### Dataset Curators |
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| | [More Information Needed] |
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| | ### Licensing Information |
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| | [More Information Needed] |
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| | ### Citation Information |
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| | [More Information Needed] |
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| | ### Contributions |
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| | [More Information Needed] |
