Update README.md

README.md

@@ -5,4 +5,146 @@ language:
 base_model:
 - facebook/dinov3-vitl16-pretrain-lvd1689m
 pipeline_tag: image-feature-extraction
----
+---
+
+
+
+
+# GR-Lite: Fashion Image Retrieval Model
+
+GR-Lite is a lightweight fashion image retrieval model fine-tuned from [DINOv3-ViT-L/16](https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m). It extracts 1024-dimensional embeddings optimized for fashion product search and retrieval tasks.
+
+GR-Lite achieves state-of-the-art (SOTA) performance on LookBench and other fashion retrieval benchmarks.See the [paper]((https://arxiv.org/abs/2601.14706)) for detailed performance metrics and comparisons.
+
+
+## Resources
+
+- 🌐 **Project Site**: [LookBench-Web](https://serendipityoneinc.github.io/look-bench-page/)
+- 📄 **Paper**: [LookBench: A Comprehensive Benchmark for Fashion Image Retrieval](https://arxiv.org/abs/2601.14706)
+- 🗃️ **Benchmark Dataset**: [LookBench on Hugging Face](https://huggingface.co/datasets/srpone/look-bench)
+- 💻 **Code & Examples**: [look-bench Code](https://github.com/SerendipityOneInc/look-bench)
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch huggingface_hub
+```
+
+For full benchmarking capabilities:
+```bash
+pip install look-bench
+```
+
+### Loading the Model
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+# Download the model checkpoint
+model_path = hf_hub_download(
+    repo_id="srpone/gr-lite",
+    filename="gr_lite.pt"
+)
+
+# Load the model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = torch.load(model_path, map_location=device)
+model.eval()
+
+print(f"Model loaded successfully on {device}")
+```
+
+### Feature Extraction
+
+```python
+# Load an image
+image = Image.open("path/to/your/image.jpg").convert("RGB")
+
+# Extract features using the model's search method
+with torch.no_grad():
+    _, embeddings = model.search(image_paths=[image], feature_dim=1024)
+
+# Convert to numpy if needed
+if isinstance(embeddings, torch.Tensor):
+    embeddings = embeddings.cpu().numpy()
+
+print(f"Feature shape: {embeddings.shape}")  # (1, 1024)
+```
+
+
+### Using with LookBench Dataset
+
+```python
+from datasets import load_dataset
+
+# Load LookBench dataset
+dataset = load_dataset("srpone/look-bench", "real_studio_flat")
+
+# Get query and gallery images
+query_image = dataset['query'][0]['image']
+gallery_image = dataset['gallery'][0]['image']
+
+# Extract features
+with torch.no_grad():
+    _, query_feat = model.search(image_paths=[query_image], feature_dim=256)
+    _, gallery_feat = model.search(image_paths=[gallery_image], feature_dim=256)
+
+# Compute similarity
+import numpy as np
+query_norm = query_feat / np.linalg.norm(query_feat)
+gallery_norm = gallery_feat / np.linalg.norm(gallery_feat)
+similarity = np.dot(query_norm, gallery_norm.T)
+print(f"Similarity: {similarity[0][0]:.4f}")
+```
+
+## Benchmark Performance
+
+GR-Lite is evaluated on the **LookBench** benchmark, which includes:
+
+- **Real Studio Flat**: Flat-lay product photos (Easy difficulty)
+- **AI-Gen Studio**: AI-generated lifestyle images (Medium difficulty)
+- **Real Streetlook**: Street fashion photos (Hard difficulty)
+- **AI-Gen Streetlook**: AI-generated street outfits (Hard difficulty)
+
+For detailed performance metrics, please refer to:
+- Paper: https://arxiv.org/abs/2601.14706
+- Benchmark: https://huggingface.co/datasets/srpone/look-bench
+
+## Evaluation
+
+Use the `look-bench` package to evaluate on LookBench:
+
+```python
+from look_bench import evaluate_model
+
+# Evaluate on all configs
+results = evaluate_model(
+    model=model,
+    model_name="gr-lite",
+    dataset_configs=["real_studio_flat", "aigen_studio", "real_streetlook", "aigen_streetlook"]
+)
+
+print(results)
+```
+
+## Model Card Authors
+
+Gensmo AI Team
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@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},
+}
+```