--- title: image_preferences_results canonical_url: "https://www.modelscope.cn/datasets/data-is-better-together/image_preferences_results" md_url: "https://www.modelscope.cn/datasets/data-is-better-together/image_preferences_results.md" repository: data-is-better-together/image_preferences_results last_updated: 2025-07-10 license: "Apache License 2.0" storage_size: "26 KB" downloads: 314 stars: 0 --- # image_preferences_results > image_preferences_results - data-is-better-together 在 ModelScope 开源的数据集。Dataset Card for imagepreferencesresults data-is-better-together/image_preferences_results 是 ModelScope 魔搭社区上的数据集，存储大小 26 KB，采用 Apache License 2.0 许可。 - **Repository**: data-is-better-together/image_preferences_results - **License**: Apache License 2.0 - **Storage size**: 26 KB - **Downloads**: 314 - **Stars**: 0 - **Last updated**: 2025-07-10 Source: https://www.modelscope.cn/datasets/data-is-better-together/image_preferences_results --- # Dataset Card for image_preferences_results 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("DIBT/image_preferences_results") ``` 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("DIBT/image_preferences_results") ``` 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 | Markdown | | ---------- | ----- | ---- | -------- | -------- | | images | images | custom | 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 | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | preference | preference | label_selection | True | Which image do you prefer given the prompt? | ['image_1', 'image_2', 'both_good', 'both_bad'] | ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda", "fields": { "images": { "image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance" } }, "id": "f5224be1-2e1b-428e-94b1-9c0f397092fa", "metadata": { "category": "Animation", "evolution": "quality", "model_1": "schnell", "model_2": "dev", "sub_category": "Pixel Art" }, "responses": { "preference": [ { "user_id": "c53e62ab-d792-4854-98f6-593b2ffb55bc", "value": "image_2" }, { "user_id": "b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3", "value": "image_2" }, { "user_id": "da3e5871-920c-44da-8c44-1e94260c581e", "value": "both_good" }, { "user_id": "b31dd1ed-78b6-4d50-8f11-7ce32ba17d64", "value": "image_2" }, { "user_id": "6b984f66-86b3-421e-a32c-cd3592ee27a1", "value": "both_bad" } ] }, "status": "completed", "suggestions": {}, "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda", "category": "Animation", "evolution": "quality", "id": "f5224be1-2e1b-428e-94b1-9c0f397092fa", "images": { "image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance" }, "model_1": "schnell", "model_2": "dev", "preference.responses": [ "image_2", "image_2", "both_good", "image_2", "both_bad" ], "preference.responses.status": [ "submitted", "submitted", "submitted", "submitted", "submitted" ], "preference.responses.users": [ "c53e62ab-d792-4854-98f6-593b2ffb55bc", "b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3", "da3e5871-920c-44da-8c44-1e94260c581e", "b31dd1ed-78b6-4d50-8f11-7ce32ba17d64", "6b984f66-86b3-421e-a32c-cd3592ee27a1" ], "status": "completed", "sub_category": "Pixel Art" } ``` ### 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]