---
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for image_net-sketch-hq-resized







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).


## 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:

```python
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:

```python
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](#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] |


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### 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](https://huggingface.co/datasets/songweig/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]