look-bench 0.2.0

A live, holistic, and challenging benchmark for fashion image retrieval in real e-commerce settings

LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval

LookBench is a live, holistic, and challenging benchmark for fashion image retrieval in real e-commerce settings. This repository provides the official evaluation code and model implementations.

📰 News

• [2026-01] LookBench paper released on arXiv
• [2026-01] GR-Lite open-source model released
• [2026-01] Initial benchmark dataset released

📖 Overview

LookBench addresses the limitations of existing fashion retrieval benchmarks by providing:

• 🔄 Continuously Refreshing Samples: Mitigates data contamination with time-stamped, periodically updated test sets
• 🎯 Diverse Retrieval Tasks: Covers single-item and multi-item retrieval across real studio, AI-generated studio, real street-look, and AI-generated street-look scenarios
• 📊 Attribute-Supervised Evaluation: Fine-grained evaluation based on 100+ fashion attributes across categories
• 🏆 Challenging Benchmarks: Many strong baselines achieve below 60% Recall@1

Benchmark Subsets

Dataset	Image Source	# Retrieval Items	Difficulty	# Queries / Corpus
RealStudioFlat	Real studio flat-lay product photos	Single	Easy	1,011 / 62,226
AIGen-Studio	AI-generated lifestyle studio images	Single	Medium	192 / 59,254
RealStreetLook	Real street outfit photos	Multi	Hard	1,000 / 61,553
AIGen-StreetLook	AI-generated street outfit compositions	Multi	Hard	160 / 58,846

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/SerendipityOneInc/look-bench.git
cd look-bench

# Install dependencies
pip install -r requirements.txt

Load Dataset from Hugging Face

The LookBench dataset is hosted on Hugging Face and can be loaded directly:

from datasets import load_dataset

# Load the entire LookBench dataset
dataset = load_dataset("srpone/look-bench")

# Access different subsets
real_studio = dataset['real_studio_flat']  # Easy: single-item retrieval
aigen_studio = dataset['aigen_studio']     # Medium: AI-generated studio images
real_street = dataset['real_streetlook']   # Hard: multi-item outfit retrieval
aigen_street = dataset['aigen_streetlook'] # Hard: AI-generated street looks

# Each subset has query and gallery splits
query_data = dataset['real_studio_flat']['query']
gallery_data = dataset['real_studio_flat']['gallery']

print(f"Query samples: {len(query_data)}")
print(f"Gallery samples: {len(gallery_data)}")

Quick Evaluation

import torch
from manager import ConfigManager, ModelManager

# Load model
config_manager = ConfigManager('configs/config.yaml')
model_manager = ModelManager(config_manager)

model, _ = model_manager.load_model('clip')
transform = model_manager.get_transform('clip')

# Extract features from an image
sample = dataset['real_studio_flat']['query'][0]
image_tensor = transform(sample['image']).unsqueeze(0)

if torch.cuda.is_available():
    model = model.cuda()
    image_tensor = image_tensor.cuda()

with torch.no_grad():
    features = model(image_tensor)

print(f"Feature shape: {features.shape}")

Run Full Evaluation

# Run evaluation with default configuration
python main.py

# Run with specific model
python main.py --pipeline evaluation --model clip

# Use custom configuration
python main.py --config configs/config.yaml

Example Scripts & Notebooks

We provide both Python scripts and Google Colab notebooks for easy experimentation:

📓 Colab Notebooks (Run in Browser)

• 01_quickstart.ipynb - Basic usage and dataset exploration
• 02_model_evaluation.ipynb - Complete evaluation pipeline
• 03_custom_model.ipynb - Integrate custom models

🐍 Python Scripts (Run Locally)

• examples/01_quickstart.py - Basic usage and dataset exploration
• examples/02_model_evaluation.py - Complete model evaluation pipeline
• examples/03_custom_model.py - Integrate your own custom models

# Run examples locally
python examples/01_quickstart.py
python examples/02_model_evaluation.py
python examples/03_custom_model.py

🏗️ Architecture

look-bench/
├── main.py                 # Main entry point (config-driven)
├── manager.py              # Configuration, model, and data managers
├── runner/                 # Pipeline execution framework
│   ├── base_pipeline.py   # Base pipeline class
│   ├── evaluator.py       # Core evaluation logic
│   ├── pipeline.py        # Pipeline registry
│   ├── evaluation_pipeline.py      # Standard evaluation pipeline
│   └── feature_extraction_pipeline.py  # Feature extraction pipeline
├── models/                 # Model implementations and registry
│   ├── base.py            # Base model interface
│   ├── registry.py        # Model registration system
│   ├── factory.py         # Model factory
│   ├── clip_model.py      # CLIP model
│   ├── siglip_model.py    # SigLIP model
│   └── dinov2_model.py    # DINOv2 model
├── datasets/               # Dataset loading (BEIR-style)
│   ├── base.py            # Base dataset implementation
│   └── registry.py        # Dataset registry
├── metrics/                # Evaluation metrics
│   ├── rank.py            # Recall@K
│   ├── mrr.py             # Mean Reciprocal Rank
│   ├── ndcg.py            # Normalized Discounted Cumulative Gain
│   └── map.py             # Mean Average Precision
├── configs/                # Configuration files
│   └── config.yaml        # Main configuration
└── utils/                  # Utilities and logging

🎯 Supported Models

Model	Architecture	Input Size	Embedding Dim	Framework
CLIP	Vision Transformer	224×224	512	PyTorch
SigLIP	Vision Transformer	224×224	768	PyTorch
DINOv2	Vision Transformer	224×224	768	PyTorch
GR-Lite	Vision Transformer	336×336	1024	PyTorch

⚙️ Configuration

Edit configs/config.yaml to configure models and evaluation settings:

# Pipeline configuration
pipeline:
  name: "evaluation"  # evaluation, feature_extraction
  model: "clip"
  dataset: "fashion200k"
  args: {}

# Model configuration
clip:
  enabled: true
  model_name: "openai/clip-vit-base-patch16"
  input_size: 224
  embedding_dim: 512
  device: "cuda"

# Evaluation settings
evaluation:
  metric: "recall"
  top_k: [1, 5, 10, 20]
  l2norm: true

📊 Evaluation Metrics

LookBench supports multiple evaluation metrics:

• Recall@K: Top-K retrieval accuracy (K=1, 5, 10, 20)
• MRR: Mean Reciprocal Rank
• NDCG@K: Normalized Discounted Cumulative Gain
• MAP: Mean Average Precision

Fine-Grained Evaluation

All metrics are computed with attribute-level matching:

• Fine Recall@1: Requires exact category and all attributes to match
• Coarse Recall@1: Only requires category to match
• nDCG@K: Graded relevance based on attribute overlap

🔧 Advanced Usage

Custom Model Integration

LookBench makes it easy to integrate your own models using the registry pattern. Here's a quick example:

from models.base import BaseModel
from models.registry import register_model
import torch.nn as nn
from torchvision import models, transforms

@register_model("resnet50", metadata={
    "description": "ResNet-50 for fashion retrieval",
    "framework": "PyTorch",
    "input_size": 224,
    "embedding_dim": 2048
})
class ResNet50Model(BaseModel):
    @classmethod
    def load_model(cls, model_name: str, model_path: str = None):
        model = models.resnet50(pretrained=True)
        model = nn.Sequential(*list(model.children())[:-1])  # Remove FC layer
        
        # Wrapper to flatten output
        class Wrapper(nn.Module):
            def __init__(self, backbone):
                super().__init__()
                self.backbone = backbone
            def forward(self, x):
                return self.backbone(x).squeeze(-1).squeeze(-1)
        
        return Wrapper(model), cls()
    
    @classmethod
    def get_transform(cls, input_size: int = 224):
        return transforms.Compose([
            transforms.Resize((input_size, input_size)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                               std=[0.229, 0.224, 0.225])
        ])

Then add your model to configs/config.yaml:

resnet50:
  enabled: true
  model_name: "resnet50"
  model_path: null  # or path to your weights
  input_size: 224
  embedding_dim: 2048
  device: "cuda"

For complete examples, see examples/03_custom_model.py

Custom Pipeline

Create custom evaluation pipelines:

from runner.base_pipeline import BasePipeline
from runner.pipeline import register_pipeline

@register_pipeline("custom_pipeline")
class CustomPipeline(BasePipeline):
    def get_pipeline_name(self) -> str:
        return "custom_pipeline"
    
    def run(self, **kwargs):
        # Your custom logic here
        model_name = kwargs.get('model_name', 'clip')
        dataset_type = kwargs.get('dataset_type', 'fashion200k')
        
        # Load model and data
        model, _ = self.model_manager.load_model(model_name)
        # ... your evaluation logic
        
        return {"status": "success", "results": results}

📈 Results

Fine Recall@1 Performance

Our GR-Lite model achieves state-of-the-art performance on LookBench. Fine Recall@1 requires exact category and all attributes to match:

Model	Resolution / Emb.	AIGen-StreetLook	AIGen-Studio	RealStreetLook	RealStudioFlat	Overall
GR-Pro (Ours)	336 / 1024	63.67	54.88	44.75	51.55	49.80
GR-Lite (Ours, Open)	336 / 1024	62.47	52.08	43.84	51.70	49.18
Marqo-FashionSigLIP	224 / 768	66.27	58.53	42.43	51.86	49.44
Marqo-FashionCLIP	224 / 512	63.22	54.93	41.87	51.68	48.63
SigLIP2-B/16	384 / 768	57.83	54.97	39.35	49.12	46.10
SigLIP2-L/16	384 / 1024	51.89	48.57	35.91	44.78	41.86
PP-ShiTuV2	224 / 512	30.06	33.69	32.77	43.22	37.17
DINOv3-ViT-L	224 / 1024	20.24	27.66	26.27	39.85	31.83
DINOv2-ViT-L	224 / 1024	24.29	25.05	22.99	37.66	29.57
CLIP-L/14	336 / 768	25.28	25.95	21.09	40.35	30.08
CLIP-B/16	224 / 512	17.86	13.75	16.80	34.75	24.36

Coarse Recall@1 Performance

Coarse Recall@1 only requires category match (more lenient):

Model	Resolution / Emb.	AIGen-StreetLook	AIGen-Studio	RealStreetLook	RealStudioFlat	Overall
GR-Pro (Ours)	336 / 1024	92.50	92.75	79.82	94.16	87.93
GR-Lite (Ours, Open)	336 / 1024	88.75	90.16	76.76	92.68	85.54
Marqo-FashionSigLIP	224 / 768	90.00	93.78	73.39	88.63	82.77
Marqo-FashionCLIP	224 / 512	84.38	87.05	75.33	88.72	82.68
SigLIP2-B/16	384 / 768	86.25	90.67	72.17	88.33	81.62
SigLIP2-L/16	384 / 1024	80.62	90.67	68.20	84.97	78.12
CLIP-L/14	336 / 768	46.88	56.48	45.26	76.85	59.91
CLIP-B/16	224 / 512	35.62	32.12	33.54	67.26	48.11

nDCG@5 Performance

nDCG@5 evaluates ranking quality with graded relevance based on attribute overlap:

Model	Resolution / Emb.	AIGen-StreetLook	AIGen-Studio	RealStreetLook	RealStudioFlat	Overall
GR-Pro (Ours)	336 / 1024	63.67	54.88	44.75	51.55	49.80
GR-Lite (Ours, Open)	336 / 1024	62.47	52.08	43.84	51.70	49.18
Marqo-FashionSigLIP	224 / 768	66.27	58.53	42.43	51.86	49.44
Marqo-FashionCLIP	224 / 512	63.22	54.93	41.87	51.68	48.63
SigLIP2-B/16	384 / 768	57.83	54.97	39.35	49.12	46.10

See our paper for complete results including MRR and additional models.

📄 Citation

If you use LookBench in your research, please cite:

@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},
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

The GR-Lite model weights are distributed under the DINOv3 License as they are derived from Meta's DINOv3 model.