README.md

metadata

license: mit
task_categories:
  - image-to-image
tags:
  - computer-vision
  - image-editing
  - exemplar-based
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: x_original
      dtype: image
    - name: x_edited
      dtype: image
    - name: y_original
      dtype: image
    - name: y_edited
      dtype: image
  splits:
    - name: train
      num_bytes: 1207235952
      num_examples: 1472
  download_size: 1142011780
  dataset_size: 1207235952

ReEdit-Bench: Benchmark Dataset for Exemplar-Based Image Editing

A curated dataset of ~1,500 samples for evaluating exemplar-based image editing methods, as presented in our WACV '25' paper - ReEdit: Multimodal Exemplar-Based Image Editing with Diffusion Models

Dataset Structure

Each sample contains 4 images representing an exemplar edit pair:

• x_original: Source image before editing
• x_edited: Source image after editing (defines the edit operation)
• y_original: Target image before editing
• y_edited: Target image after the same edit is applied

Dataset Description

This dataset was carefully curated from InstructP2P samples, with manual visual inspection to ensure high quality. The edit operation demonstrated on (x_original → x_edited) should be apllied to y_original. y_edit denotes the gold standard ground truth for the edited target

Usage

from datasets import load_dataset

dataset = load_dataset("{dataset_name}")
sample = dataset[0]

# Access images
x_orig = sample['x_original']
x_edit = sample['x_edited'] 
y_orig = sample['y_original']
y_edit = sample['y_edited']

Citation

If you use this dataset, please cite the associated paper

@InProceedings{Srivastava_2025_WACV,
    author    = {Srivastava, Ashutosh and Menta, Tarun Ram and Java, Abhinav and Jadhav, Avadhoot Gorakh and Singh, Silky and Jandial, Surgan and Krishnamurthy, Balaji},
    title     = {ReEdit: Multimodal Exemplar-Based Image Editing},
    booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)},
    month     = {February},
    year      = {2025},
    pages     = {929-939}
}