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--- |
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pretty_name: Pick-a-Pic v2 · FiFA Filtered |
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license: mit |
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dataset_info: |
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- config_name: FiFA-100k |
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features: |
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- name: are_different |
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dtype: bool |
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- name: best_image_uid |
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dtype: string |
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- name: caption |
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dtype: string |
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- name: created_at |
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dtype: timestamp[ns] |
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- name: has_label |
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dtype: bool |
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- name: image_0_uid |
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dtype: string |
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- name: image_0_url |
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dtype: string |
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- name: image_1_uid |
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dtype: string |
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- name: image_1_url |
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dtype: string |
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- name: jpg_0 |
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dtype: binary |
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- name: jpg_1 |
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dtype: binary |
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- name: label_0 |
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dtype: float64 |
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- name: label_1 |
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dtype: float64 |
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- name: model_0 |
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dtype: string |
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- name: model_1 |
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dtype: string |
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- name: ranking_id |
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dtype: int64 |
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- name: user_id |
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dtype: int64 |
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- name: num_example_per_prompt |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 32217070275.579575 |
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num_examples: 95948 |
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download_size: 32218306878 |
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dataset_size: 32217070275.579575 |
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- config_name: FiFA-10k |
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features: |
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- name: are_different |
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dtype: bool |
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- name: best_image_uid |
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dtype: string |
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- name: caption |
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dtype: string |
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- name: created_at |
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dtype: timestamp[ns] |
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- name: has_label |
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dtype: bool |
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- name: image_0_uid |
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dtype: string |
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- name: image_0_url |
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dtype: string |
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- name: image_1_uid |
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dtype: string |
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- name: image_1_url |
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dtype: string |
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- name: jpg_0 |
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dtype: binary |
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- name: jpg_1 |
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dtype: binary |
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- name: label_0 |
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dtype: float64 |
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- name: label_1 |
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dtype: float64 |
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dtype: string |
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- name: model_1 |
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dtype: string |
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- name: ranking_id |
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dtype: int64 |
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- name: user_id |
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dtype: int64 |
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- name: num_example_per_prompt |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 3218416419.2211432 |
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num_examples: 9585 |
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download_size: 3216732485 |
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dataset_size: 3218416419.2211432 |
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- config_name: FiFA-1k |
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features: |
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- name: are_different |
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dtype: bool |
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- name: best_image_uid |
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dtype: string |
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- name: caption |
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dtype: string |
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- name: created_at |
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dtype: timestamp[ns] |
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- name: has_label |
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dtype: bool |
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- name: image_0_uid |
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dtype: string |
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- name: image_0_url |
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dtype: string |
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- name: image_1_uid |
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dtype: string |
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- name: image_1_url |
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dtype: string |
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- name: jpg_0 |
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dtype: binary |
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- name: jpg_1 |
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dtype: binary |
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- name: label_0 |
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dtype: float64 |
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- name: label_1 |
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dtype: float64 |
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dtype: string |
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- name: model_1 |
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dtype: string |
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- name: ranking_id |
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dtype: int64 |
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- name: user_id |
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dtype: int64 |
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- name: num_example_per_prompt |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 322345306.4634635 |
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num_examples: 960 |
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download_size: 318043276 |
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dataset_size: 322345306.4634635 |
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- config_name: FiFA-20k |
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features: |
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- name: are_different |
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dtype: bool |
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- name: best_image_uid |
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dtype: string |
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- name: caption |
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dtype: string |
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- name: created_at |
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dtype: timestamp[ns] |
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- name: has_label |
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dtype: bool |
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- name: image_0_uid |
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dtype: string |
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- name: image_0_url |
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dtype: string |
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- name: image_1_uid |
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dtype: string |
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- name: image_1_url |
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dtype: string |
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- name: jpg_0 |
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dtype: binary |
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- name: jpg_1 |
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dtype: binary |
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dtype: float64 |
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dtype: float64 |
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dtype: string |
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dtype: string |
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- name: ranking_id |
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dtype: int64 |
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- name: user_id |
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dtype: int64 |
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- name: num_example_per_prompt |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 6444891471.103872 |
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num_examples: 19194 |
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download_size: 6450692192 |
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dataset_size: 6444891471.103872 |
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- config_name: FiFA-500 |
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features: |
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- name: are_different |
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dtype: bool |
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- name: best_image_uid |
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|
dtype: string |
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- name: caption |
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dtype: string |
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- name: created_at |
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dtype: timestamp[ns] |
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- name: has_label |
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dtype: bool |
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- name: image_0_uid |
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dtype: string |
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- name: image_0_url |
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dtype: string |
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- name: image_1_uid |
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dtype: string |
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- name: image_1_url |
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dtype: string |
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- name: jpg_0 |
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dtype: binary |
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- name: jpg_1 |
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dtype: binary |
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- name: label_0 |
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dtype: float64 |
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- name: label_1 |
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dtype: float64 |
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- name: model_0 |
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dtype: string |
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- name: model_1 |
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dtype: string |
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- name: ranking_id |
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dtype: int64 |
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- name: user_id |
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dtype: int64 |
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- name: num_example_per_prompt |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 162515758.6753295 |
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num_examples: 484 |
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download_size: 160479513 |
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dataset_size: 162515758.6753295 |
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- config_name: FiFA-50k |
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features: |
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|
- name: are_different |
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|
dtype: bool |
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|
- name: best_image_uid |
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|
dtype: string |
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- name: caption |
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dtype: string |
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- name: created_at |
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dtype: timestamp[ns] |
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- name: has_label |
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dtype: bool |
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- name: image_0_uid |
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dtype: string |
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- name: image_0_url |
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dtype: string |
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- name: image_1_uid |
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dtype: string |
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- name: image_1_url |
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dtype: string |
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- name: jpg_0 |
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dtype: binary |
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- name: jpg_1 |
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dtype: binary |
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- name: label_0 |
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dtype: float64 |
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- name: label_1 |
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dtype: float64 |
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- name: model_0 |
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dtype: string |
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- name: model_1 |
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dtype: string |
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- name: ranking_id |
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dtype: int64 |
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- name: user_id |
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dtype: int64 |
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- name: num_example_per_prompt |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 16102155386.9327 |
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num_examples: 47955 |
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download_size: 16114773526 |
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dataset_size: 16102155386.9327 |
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- config_name: FiFA-5k |
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features: |
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- name: are_different |
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dtype: bool |
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- name: best_image_uid |
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dtype: string |
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- name: caption |
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dtype: string |
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- name: created_at |
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dtype: timestamp[ns] |
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- name: has_label |
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dtype: bool |
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- name: image_0_uid |
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dtype: string |
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- name: image_0_url |
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dtype: string |
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- name: image_1_uid |
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dtype: string |
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- name: image_1_url |
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dtype: string |
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- name: jpg_0 |
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dtype: binary |
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- name: jpg_1 |
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dtype: binary |
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- name: label_0 |
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dtype: float64 |
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dtype: float64 |
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dtype: string |
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dtype: string |
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- name: ranking_id |
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dtype: int64 |
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- name: user_id |
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dtype: int64 |
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- name: num_example_per_prompt |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 1606018334.1820269 |
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num_examples: 4783 |
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download_size: 1592623798 |
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dataset_size: 1606018334.1820269 |
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configs: |
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- config_name: FiFA-100k |
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data_files: |
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- split: train |
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path: FiFA-100k/train-* |
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- config_name: FiFA-10k |
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data_files: |
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- split: train |
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path: FiFA-10k/train-* |
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- config_name: FiFA-1k |
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data_files: |
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- split: train |
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path: FiFA-1k/train-* |
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- config_name: FiFA-20k |
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data_files: |
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- split: train |
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path: FiFA-20k/train-* |
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- config_name: FiFA-500 |
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data_files: |
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- split: train |
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path: FiFA-500/train-* |
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- config_name: FiFA-50k |
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data_files: |
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- split: train |
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path: FiFA-50k/train-* |
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- config_name: FiFA-5k |
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data_files: |
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- split: train |
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path: FiFA-5k/train-* |
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default: true |
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--- |
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# Pick-a-Pic v2 · **FiFA** Filtered Subsets |
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These subsets were produced by filtering the original [Pick-a-Pic v2 dataset](https://arxiv.org/abs/2305.01569) using **FiFA**, a data filtering algorithm proposed in the paper [*Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models*](https://arxiv.org/abs/2410.10166). |
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## Overview |
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The filtering process is based on three key metrics: |
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1. **Preference Margin**: Estimated using [PickScore](https://huggingface.co/yuvalkirstain/PickScore_v1) |
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2. **Text Quality**: Estimated through LLM scoring |
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3. **Text Diversity**: Estimated using K-NN distance |
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## Dataset Configurations |
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This dataset provides several configurations, each corresponding to a different number of filtered triplets selected by the automated FiFA algorithm. You can choose from `FiFA-500`, `FiFA-1k`, `FiFA-5k`, `FiFA-10k`, `FiFA-20k`, `FiFA-50k`, and `FiFA-100k`, depending on your needs. The only difference between these configurations is the number of examples included; all are filtered using the same FiFA method. We recommend using the `FiFA-5k` configuration as the default, as it generally works best for Stable Diffusion training. |
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## Quick Start |
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```python |
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from datasets import load_dataset |
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# Load a specific configuration |
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dataset = load_dataset("Dragonjinny/FiFA-pickapic-v2", "FiFA-5k", split="train") |
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# Access the data |
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import io |
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from PIL import Image |
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for example in dataset: |
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caption = example["caption"] # The prompt text |
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jpg_0 = example["jpg_0"] # First image (bytes) |
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jpg_1 = example["jpg_1"] # Second image (bytes) |
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label_0 = example["label_0"] # Binary label (0 or 1) indicating which image is preferred |
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# Convert bytes to PIL Images |
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image1 = Image.open(io.BytesIO(jpg_0)).convert("RGB") |
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image2 = Image.open(io.BytesIO(jpg_1)).convert("RGB") |
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# Now you can work with the images |
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print(f"Caption: {caption}") |
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print(f"Preferred image: {'jpg_0' if label_0 == 1 else 'jpg_1'}") |
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# image1.show() # Display the first image |
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# image2.show() # Display the second image |
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``` |
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## Data Format |
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Each example contains: |
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- `caption`: The prompt text |
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- `jpg_0`: First image (bytes) |
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- `jpg_1`: Second image (bytes) |
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- `label_0`: Binary label (0 or 1) indicating which image is preferred |
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- `__index_level_0__` : Unique ID for each data point |
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## Citation |
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If you use this dataset in your research, please cite our paper: |
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```bibtex |
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@inproceedings{ |
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yang2025automated, |
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title={Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models}, |
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author={Yongjin Yang and Sihyeon Kim and Hojung Jung and Sangmin Bae and SangMook Kim and Se-Young Yun and Kimin Lee}, |
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booktitle={The Thirteenth International Conference on Learning Representations}, |
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year={2025}, |
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url={https://openreview.net/forum?id=8jvVNPHtVJ} |
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} |
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``` |
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## License |
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This dataset is licensed under [MIT License](https://opensource.org/licenses/MIT), following the license of the original Pick-a-Pic v2 dataset. |
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