Dataset Card for image_net-sketch-hq-resized

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("tumuyan2/image_net-sketch-hq-resized", 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("tumuyan2/image_net-sketch-hq-resized")

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
jpeg	jpeg	image	False
key	key	text	False
url	url	text	False

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
rating_0	quality	rating	True	detail & quality score.
0-1 image will be delete
0: trash, not sketch, pixelate;
1: simple image;
3: like illustrations from 1990s books;
5: perfect sketch	[0, 1, 2, 3, 4, 5]
rating_1	watermark	rating	True	0: negative watermark,
1: simple watermark, could be croped,
2: no watermark	[0, 1, 2]

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

This is a selection from imagenet_sketch, I will remove images with watermarks or Low-quality according to Annotation.
Dataset with annotation will push to https://huggingface.co/datasets/tumuyan2/image_net-sketch-hq-resized everyday.

Quality(0-1 image will be delete)

• 0: trash, not sketch, pixelate, photo;
• 1: simple image, like logo;
• 3: like illustrations from 1990s books;
• 5: perfect sketch

Watermark

• 0: negative watermark (in the center or repeat in the full image. care for light gray watermarks)
• 1: simple watermark, could be croped
• 2: watermark not in main area

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

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

[More Information Needed]