Update README.md

README.md

@@ -1,8 +1,39 @@
 ---
 license: apache-2.0
 tags:
-- feature-matching
 - computer-vision
+- feature-matching
 - onnx
-- spatialhub
----
+- efficient-loftr
+pipeline_tag: image-feature-extraction
+---
+
+# EfficientLoFTR ONNX Weights
+
+This repository contains the ONNX-optimized weights for **EfficientLoFTR**, a model used for finding matching points between pairs of images. 
+
+By converting the original PyTorch model weights into the ONNX format, these files allow you to run fast feature-matching inference on both CPU and GPU without needing to install the heavy PyTorch framework.
+
+## Available Files
+* **`eloftr_outdoor_full.onnx`**: The standard version of the model, optimized for the best matching quality.
+* **`eloftr_outdoor_opt.onnx`**: An efficiency-focused version of the model, optimized for faster inference speed.
+
+---
+
+## How to Use
+
+The easiest way to load and use these files is through the **[spatialhub](https://github.com/pankajkaushik12/spatialhub)** Python library.
+
+
+## Original Citation
+If you use these models in academic work, please cite the original authors:
+```bibtex
+@inproceedings{wang2022efficientloftr,
+  title={EfficientLoFTR: Semi-Dense Local Feature Matching with Sparse Transformers},
+  author={Wang, Yanzhao and Geng, Yuwei and Jiang, Zheng and Zhao, Yihong and Jin, Shisheng and Lin, Siyu and Han, Feng},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year={2022}
+}
+```
+
+**Note**: This repository provides pre-converted weights for inference purposes.