README.md

---
license: apache-2.0
task_categories:
  - image-feature-extraction
  - zero-shot-image-classification
language:
  - en
tags:
  - fashion
  - image-retrieval
  - benchmark
  - e-commerce
  - visual-search
pretty_name: LookBench
size_categories:
  - 10K<n<100K
configs:
  - config_name: aigen_streetlook
    data_files:
      - split: query
        path: "v20251201/aigen_streetlook/query.parquet"
      - split: gallery
        path: "v20251201/aigen_streetlook/gallery.parquet"
  - config_name: aigen_studio
    data_files:
      - split: query
        path: "v20251201/aigen_studio/query.parquet"
      - split: gallery
        path: "v20251201/aigen_studio/gallery.parquet"
  - config_name: real_streetlook
    data_files:
      - split: query
        path: "v20251201/real_streetlook/query.parquet"
      - split: gallery
        path: "v20251201/real_streetlook/gallery.parquet"
  - config_name: real_studio_flat
    data_files:
      - split: query
        path: "v20251201/real_studio_flat/query.parquet"
      - split: gallery
        path: "v20251201/real_studio_flat/gallery.parquet"
  - config_name: noise
    data_files:
      - split: gallery
        path: "v20251201/noise/*.parquet"
dataset_info:
  features:
    - name: image
      dtype: image
    - name: category
      dtype: string
    - name: main_attribute
      dtype: string
    - name: other_attributes
      dtype: string
    - name: bbox
      dtype: string
    - name: item_ID
      dtype: string
    - name: task
      dtype: string
    - name: difficulty
      dtype: string
---

# LookBench: A Live and Holistic Fashion Image Retrieval Benchmark

**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.

This dataset accompanies the paper [LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval](https://arxiv.org/abs/2601.14706).

[project page](https://serendipityoneinc.github.io/look-bench-page/)

[code](https://github.com/SerendipityOneInc/look-bench)



## 🎯 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:

```python
from datasets import load_dataset

dataset = load_dataset("srpone/look-bench")
print(dataset)
```


## Citation 

```
@article{gao2026lookbench,
      title={LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval}, 
      author={Chao Gao and Siqiao Xue and Yimin Peng and Jiwen Fu and Tingyi Gu and Shanshan Li and Fan Zhou},
      year={2026},
      url={https://arxiv.org/abs/2601.14706}, 
      journal= {arXiv preprint arXiv:2601.14706},
}
```