HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based
  Image Editing
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.
6752613e5d8bc6e501691fee
Jinbin Bai
claimed_verified
2024-12-06T09:09:25.700Z
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Jinbin Bai
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BryanW
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Wei Chow
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2024-12-06T09:09:23.481Z
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zhou wei
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WeiChow
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Ling Yang
claimed_verified
2025-02-11T15:34:12.918Z
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Ling Yang
user
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Xiangtai Li
admin_assigned
2024-12-06T17:34:27.177Z
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Xiangtai Li
user
LXT
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Juncheng Li
6752613e5d8bc6e501691ff3
Hanwang Zhang
6752613e5d8bc6e501691ff4
Shuicheng Yan
HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based
  Image Editing
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.
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