metadata

dataset_info:
  features:
    - name: are_different
      dtype: bool
    - name: best_image_uid
      dtype: string
    - name: caption
      dtype: string
    - name: created_at
      dtype: timestamp[ns]
    - name: has_label
      dtype: bool
    - name: image_0_uid
      dtype: string
    - name: image_0_url
      dtype: string
    - name: image_1_uid
      dtype: string
    - name: image_1_url
      dtype: string
    - name: jpg_0
      dtype: binary
    - name: jpg_1
      dtype: binary
    - name: label_0
      dtype: float64
    - name: label_1
      dtype: float64
    - name: model_0
      dtype: string
    - name: model_1
      dtype: string
    - name: ranking_id
      dtype: int64
    - name: user_id
      dtype: int64
    - name: num_example_per_prompt
      dtype: int64
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: train
      num_bytes: 193273338802
      num_examples: 583747
    - name: validation
      num_bytes: 5638295249
      num_examples: 17439
    - name: test
      num_bytes: 4621428929
      num_examples: 14073
    - name: validation_unique
      num_bytes: 178723392
      num_examples: 500
    - name: test_unique
      num_bytes: 178099641
      num_examples: 500
  download_size: 202289408791
  dataset_size: 203889886013

Dataset Card for Pick-a-Pic (v1)

Dataset Description

Homepage: The web app can be found at pickapic.io
Repository: The repository of PickScore
Paper: Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation.
**Leaderboard: TODO **
**Point of Contact: TODO **

Dataset Summary

The Pick-a-Pic dataset was collected with the Pick-a-Pic web app and contains over half-a-million examples of human preferences over model-generated images. This dataset with URLs instead of the actual images (which makes it much smaller in size) can be found here.

See the corresponding paper Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation for more details.

Supported Tasks and Leaderboards

Task: Select preferred image in test-set.

Models	Test-Set Accuracy (%)
PickScore	70.2%
Human Expert Baseline	68.0%
HPS	66.7%
ImageReward	61.1%
CLIP-H	60.8%
Aesthetics	56.8%

Data Splits

The dataset has three main splits: train, validation, validation_unique (with one example per prompt), test, and test_unique.

Citation Information

If you find this work useful, please cite:

@inproceedings{Kirstain2023PickaPicAO,
  title={Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation},
  author={Yuval Kirstain and Adam Polyak and Uriel Singer and Shahbuland Matiana and Joe Penna and Omer Levy},
  year={2023}
}

LICENSE

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.