title: HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based
    Image Editing
summary: We present HumanEdit, a high-quality, human-rewarded dataset specifically
  designed for instruction-guided image editing, enabling precise and diverse
  image manipulations through open-form language instructions. Previous
  large-scale editing datasets often incorporate minimal human feedback, leading
  to challenges in aligning datasets with human preferences. HumanEdit bridges
  this gap by employing human annotators to construct data pairs and
  administrators to provide feedback. With meticulously curation, HumanEdit
  comprises 5,751 images and requires more than 2,500 hours of human effort
  across four stages, ensuring both accuracy and reliability for a wide range of
  image editing tasks. The dataset includes six distinct types of editing
  instructions: Action, Add, Counting, Relation, Remove, and Replace,
  encompassing a broad spectrum of real-world scenarios. All images in the
  dataset are accompanied by masks, and for a subset of the data, we ensure that
  the instructions are sufficiently detailed to support mask-free editing.
  Furthermore, HumanEdit offers comprehensive diversity and high-resolution 1024
  times 1024 content sourced from various domains, setting a new versatile
  benchmark for instructional image editing datasets. With the aim of advancing
  future research and establishing evaluation benchmarks in the field of image
  editing, we release HumanEdit at
  https://huggingface.co/datasets/BryanW/HumanEdit.
paper_authors._id: 6752613e5d8bc6e501691fee
paper_authors.name: Jinbin Bai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T09:09:25.700Z
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paper_authors.fullname: Jinbin Bai
paper_authors.type: user
paper_authors.user: BryanW
paper_authors._id: 6752613e5d8bc6e501691fef
paper_authors.name: Wei Chow
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T09:09:23.481Z
paper_authors._id: 644b71ddb2e7823a76abcf91
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644b71ddb2e7823a76abcf91/JPF7Eqeq2jx8i79nQ962K.jpeg
paper_authors.fullname: zhou wei
paper_authors.type: user
paper_authors.user: WeiChow
paper_authors._id: 6752613e5d8bc6e501691ff0
paper_authors.name: Ling Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T15:34:12.918Z
paper_authors._id: 64fde4e252e82dd432b74ce9
paper_authors.avatarUrl: /avatars/061a69d858b86d1600be916122cae7fc.svg
paper_authors.fullname: Ling Yang
paper_authors.type: user
paper_authors.user: Lingaaaaaaa
paper_authors._id: 6752613e5d8bc6e501691ff1
paper_authors.name: Xiangtai Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-06T17:34:27.177Z
paper_authors._id: 63958b4414513eaf9029ebf1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
paper_authors.fullname: Xiangtai Li
paper_authors.type: user
paper_authors.user: LXT
paper_authors._id: 6752613e5d8bc6e501691ff2
paper_authors.name: Juncheng Li
paper_authors._id: 6752613e5d8bc6e501691ff3
paper_authors.name: Hanwang Zhang
paper_authors._id: 6752613e5d8bc6e501691ff4
paper_authors.name: Shuicheng Yan
paper_title: HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based
    Image Editing
paper_summary: We present HumanEdit, a high-quality, human-rewarded dataset specifically
  designed for instruction-guided image editing, enabling precise and diverse
  image manipulations through open-form language instructions. Previous
  large-scale editing datasets often incorporate minimal human feedback, leading
  to challenges in aligning datasets with human preferences. HumanEdit bridges
  this gap by employing human annotators to construct data pairs and
  administrators to provide feedback. With meticulously curation, HumanEdit
  comprises 5,751 images and requires more than 2,500 hours of human effort
  across four stages, ensuring both accuracy and reliability for a wide range of
  image editing tasks. The dataset includes six distinct types of editing
  instructions: Action, Add, Counting, Relation, Remove, and Replace,
  encompassing a broad spectrum of real-world scenarios. All images in the
  dataset are accompanied by masks, and for a subset of the data, we ensure that
  the instructions are sufficiently detailed to support mask-free editing.
  Furthermore, HumanEdit offers comprehensive diversity and high-resolution 1024
  times 1024 content sourced from various domains, setting a new versatile
  benchmark for instructional image editing datasets. With the aim of advancing
  future research and establishing evaluation benchmarks in the field of image
  editing, we release HumanEdit at
  https://huggingface.co/datasets/BryanW/HumanEdit.
paper_ai_keywords: instruction-guided image editing human-rewarded dataset open-form language instructions human annotators human feedback Action Add Counting Relation Remove Replace mask-free editing high-resolution $1024 \times 1024$ content versatile benchmark instructional image editing datasets