---
license: apache-2.0
language:
- en
base_model:
- facebook/dinov3-vitl16-pretrain-lvd1689m
pipeline_tag: image-feature-extraction
tags:
- fashion
- image-retrieval
- vision-transformer
- dino
library_name: transformers
---

# GR-Lite: Fashion Image Retrieval Model

GR-Lite is a fashion image retrieval model fine-tuned from [DINOv3-ViT-L/16](https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m). It extracts 1024-dimensional L2-normalized embeddings optimized for fashion product search and retrieval.

GR-Lite achieves state-of-the-art performance on [LookBench](https://huggingface.co/datasets/srpone/look-bench) and other fashion retrieval benchmarks. See the [paper](https://arxiv.org/abs/2601.14706) for detailed metrics.

| Property | Value |
|----------|-------|
| Architecture | ViT-L/16 (DINOv2-style) |
| Parameters | 303M |
| Input | 336 x 336 RGB |
| Output | 1024-dim L2-normalized embedding |
| Framework | PyTorch / Transformers |


** Update: The pro version can be accessed via **https://apiclaw.io/en/api-docs** with /openapi/v2/model/fashion-image-embedding endpoint. **  



## Quick Start

```python
from transformers import AutoModel
import torch
from PIL import Image
from torchvision import transforms

# Load model
model = AutoModel.from_pretrained("srpone/gr-lite", trust_remote_code=True)
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Resize((336, 336)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

image = Image.open("your_image.jpg").convert("RGB")
pixel_values = transform(image).unsqueeze(0)  # [1, 3, 336, 336]

# Extract embedding
with torch.no_grad():
    output = model(pixel_values)
    embedding = output.pooler_output  # [1, 1024], L2-normalized

print(f"Embedding shape: {embedding.shape}")
```

## Feature Extraction (Batch)

```python
from torch.utils.data import DataLoader, Dataset

class ImageDataset(Dataset):
    def __init__(self, image_paths, transform):
        self.image_paths = image_paths
        self.transform = transform

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        image = Image.open(self.image_paths[idx]).convert("RGB")
        return self.transform(image)

# Create dataloader
dataset = ImageDataset(your_image_paths, transform)
loader = DataLoader(dataset, batch_size=64, num_workers=4)

# Extract all embeddings
all_embeddings = []
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

with torch.no_grad():
    for batch in loader:
        output = model(batch.to(device))
        all_embeddings.append(output.pooler_output.cpu())

embeddings = torch.cat(all_embeddings, dim=0)  # [N, 1024]
```

## Image Retrieval

```python
import torch.nn.functional as F

# query_emb: [1, 1024], gallery_embs: [N, 1024]
similarity = query_emb @ gallery_embs.T  # cosine similarity (already L2-normalized)
top_k_indices = similarity.argsort(descending=True)[0, :10]
```

## Model Outputs

The model returns a `BaseModelOutputWithPooling`:

| Field | Shape | Description |
|-------|-------|-------------|
| `pooler_output` | `[B, 1024]` | L2-normalized CLS token embedding (use this for retrieval) |
| `last_hidden_state` | `[B, 446, 1024]` | Full sequence output (CLS + 4 registers + 441 patches) |

## Resources

- **Project Site**: [LookBench](https://serendipityoneinc.github.io/look-bench-page/)
- **Paper**: [LookBench: A Comprehensive Benchmark for Fashion Image Retrieval](https://arxiv.org/abs/2601.14706)
- **Benchmark Dataset**: [srpone/look-bench](https://huggingface.co/datasets/srpone/look-bench)
- **Code & Notebooks**: [look-bench GitHub](https://github.com/SerendipityOneInc/look-bench)

## Citation

```bibtex
@article{gao2026lookbench,
      title={LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval},
      author={Chao Gao and Siqiao Xue 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},
}
```