Update README.md

README.md

@@ -63,11 +63,77 @@ dataset_info:
       dtype: string
 ---
 
-## How to Use
+# LookBench: A Live and Holistic Fashion Image Retrieval Benchmark
 
-LookBench is designed for evaluating **fashion image retrieval** models under realistic and contamination-aware settings. Each subset consists of a **query set** and a large **corpus (gallery)** used for retrieval evaluation.
+**LookBench** is a large-scale, open benchmark for **fashion image retrieval**, designed to evaluate modern vision and vision–language models under realistic, contamination-aware settings. The benchmark emphasizes *live data*, *domain diversity*, and *holistic retrieval tasks* spanning both single-item and outfit-level scenarios.
 
-### 1. Load the Dataset
+This dataset accompanies the paper **LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval**.
+
+---
+
+
+## 🎯 Motivation
+
+Existing fashion retrieval benchmarks often suffer from:
+- Significant test–training contamination
+- Over-reliance on clean studio product images
+- Limited support for outfit-level and real-world queries
+
+LookBench addresses these limitations by introducing **live, recently collected images**, **street-style outfit queries**, and **AI-generated images**, enabling more realistic and forward-looking evaluation.
+
+---
+
+## 📦 Dataset Overview
+
+LookBench consists of multiple subsets reflecting different image sources and retrieval difficulties.  
+Each subset is constructed as a **query–corpus retrieval benchmark**, where query images are matched against a large gallery.
+
+### Subsets (from Table 1 in the paper)
+
+| Subset Name           | Image Source                        | Retrieval Type | Difficulty | #Queries | #Corpus |
+|-----------------------|------------------------------------|---------------:|-----------:|---------:|--------:|
+| RealStudioFlat        | Real studio flat-lay product images | Single-item    | Easy       | 1,011    | 62,226  |
+| AIGen-Studio          | AI-generated studio images         | Single-item    | Medium     | 192      | 59,254  |
+| RealStreetLook        | Real street outfit images          | Multi-item     | Hard       | 1,000    | 61,553  |
+| AIGen-StreetLook      | AI-generated street outfit images  | Multi-item     | Hard       | 160      | 58,846  |
+
+---
+
+## 🧠 Tasks
+
+LookBench supports two primary retrieval tasks:
+
+### 1. Single-Item Retrieval
+Given a query image containing a single fashion item, retrieve the exact matching product from the corpus.
+
+### 2. Multi-Item (Outfit) Retrieval
+Given a street-style image containing multiple fashion items, retrieve **all corresponding products** from the corpus.
+
+These tasks reflect real-world fashion search and recommendation scenarios.
+
+---
+
+## 🧾 Data Format
+
+Each dataset subset contains:
+
+- **Query split**: images used as retrieval queries  
+- **Corpus split**: candidate images used as the retrieval gallery  
+
+Each sample may include the following fields (subset-dependent):
+
+- `image`: Input fashion image
+- `category`: Fashion category label
+- `bbox`: Bounding box of the fashion item
+- `item_id`: Unique product identifier
+- `task`: Retrieval task type
+- `difficulty`: Difficulty level
+
+---
+
+## 🚀 How to Use
+
+### Load the Dataset
 
 You can load LookBench using the 🤗 Datasets library:
 
@@ -76,5 +142,7 @@ from datasets import load_dataset
 
 dataset = load_dataset("srpone/look-bench")
 print(dataset)
+```
+