Update README.md

README.md

@@ -32,7 +32,7 @@ dataset_info:
   - name: id
     dtype: int32
   splits:
-  - name: train
+  - name: test
     num_bytes: 4106538055.5
     num_examples: 1277
   download_size: 703956134
@@ -40,6 +40,62 @@ dataset_info:
 configs:
 - config_name: default
   data_files:
-  - split: train
+  - split: test
     path: data/train-*
+task_categories:
+- image-to-image
+language:
+- en
+tags:
+- Exemplar
+- Editing
+- Image2Image
+- Diffusion
+pretty_name: Top-Bench-X
+size_categories:
+- 1K<n<10K
 ---
+
+#  EditCLIP: Representation Learning for Image Editing
+<div align="center">
+
+[📑 Paper](https://arxiv.org/abs/2503.20318) 
+[💻 Project Page](https://qianwangx.github.io/EditCLIP/)
+[🐙 Github](https://github.com/QianWangX/EditCLIP) 
+
+</div>
+
+## 📚 Introduction
+The **TOP-Bench-X** dataset offers **Query** and **Exemplar** image pairs tailored for exemplar-based image editing. We built it by adapting the TOP-Bench dataset from [InstructBrush](https://royzhao926.github.io/InstructBrush/). Specifically, we use the original training split to generate exemplar images and the test split to supply their corresponding queries. In total, TOP-Bench-X comprises **1,277** samples, including **257** distinct exemplars and **124** unique queries.
+
+<img src="assets/teaser_editclip.png" alt="Teaser figure of EditCLIP" width="100%">
+
+## 💡 Abstract
+
+We introduce EditCLIP, a novel representation-learning approach for image editing. Our method learns a unified representation of edits by jointly encoding an input image and its edited counterpart, effectively capturing their transformation. To evaluate its effectiveness, we employ EditCLIP to solve two tasks: exemplar-based image editing and automated edit evaluation. In exemplar-based image editing, we replace text-based instructions in InstructPix2Pix with EditCLIP embeddings computed from a reference exemplar image pair. Experiments demonstrate that our approach outperforms state-of-the-art methods while being more efficient and versatile. For automated evaluation, EditCLIP assesses image edits by measuring the similarity between the EditCLIP embedding of a given image pair and either a textual editing instruction or the EditCLIP embedding of another reference image pair. Experiments show that EditCLIP aligns more closely with human judgments than existing CLIP-based metrics, providing a reliable measure of edit quality and structural preservation.
+
+
+## 🧠 Data explained
+
+Each sample consists of 4 images (2 pairs of images) and metadata, specifically:
+
+1. *input_test* – the query image \(I_q\) from the test split (“before” image you want to edit)  
+2. *input_gt* – the ground-truth edited version of that query image (“after” image for the test)  
+3. *exemplar_input* – the exemplar’s input image \(I_i\) from the training split (“before” image of the exemplar)  
+4. *exemplar_edit* – the exemplar’s edited image \(I_e\) from the training split (“after” image of the exemplar)  
+
+## 🌟 Citation
+
+```bibtex
+@article{wang2025editclip,
+  title={EditCLIP: Representation Learning for Image Editing},
+  author={Wang, Qian and Cvejic, Aleksandar and Eldesokey, Abdelrahman and Wonka, Peter},
+  journal={arXiv preprint arXiv:2503.20318},
+  year={2025}
+}
+```
+
+## 💳 License
+
+This dataset is mainly a variation of TOP-Bench, confirm the license from the original authors. 
+