tarun-menta/re-edit-bench

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}
}