Improve model card and add metadata

README.md

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----
-license: mit
----
+---
+license: mit
+library_name: pytorch
+pipeline_tag: image-to-image
+tags:
+  - medical-imaging
+  - registration
+  - 3d-registration
+  - x-ray
+  - ct
+  - mri
+---
+
+# `xvr`: X-ray to Volume Registration
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
+<a href="https://colab.research.google.com/drive/1K9lBPxcLh55mr8o50Y7aHkjzjEWKPCrM?usp=sharing"><img alt="Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a>
+<a href="https://huggingface.co/eigenvivek/xvr/tree/main" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-ffc107?color=ffc107&logoColor=white"/></a>
+<a href="https://huggingface.co/datasets/eigenvivek/xvr-data/tree/main" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Data-ffc107?color=ffc107&logoColor=white"/></a>
+[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
+
+**`xvr` is a PyTorch package for training, fine-tuning, and performing 2D/3D X-ray to CT/MR registration using pose regression models.**  It provides a streamlined CLI and API for training patient-specific registration models efficiently.  Key features include significantly faster training than comparable methods, submillimeter registration accuracy, and human-interpretable pose parameters.
+
+
+<p align="center">
+  <img width="410" alt="image" src="https://github.com/user-attachments/assets/8a01c184-f6f1-420e-82b9-1cbe733adf7f" />
+</p>
+
+## Key Features
+
+- 🚀 Single CLI/API for training and registration.
+- ⚡️ Significantly faster training than existing methods.
+- 📐 Submillimeter registration accuracy.
+- 🩺 Human-interpretable pose parameters.
+- 🐍 Pure Python/PyTorch implementation.
+- 🖥️ Cross-platform support (macOS, Linux, Windows).
+
+`xvr` leverages [`DiffDRR`](https://github.com/eigenvivek/DiffDRR), the differentiable X-ray renderer.
+
+## Installation and Usage
+
+Refer to the [GitHub repository](https://github.com/eigenvivek/xvr) for detailed installation instructions, usage examples, and documentation on training, finetuning, and registration.
+
+
+## Experiments
+
+#### Models
+
+Pretrained models are available [here](https://huggingface.co/eigenvivek/xvr/tree/main).
+
+#### Data
+
+Benchmarks datasets, reformatted into DICOM/NIfTI files, are available [here](https://huggingface.co/datasets/eigenvivek/xvr-data/tree/main).
+
+If you use the [`DeepFluoro`](https://github.com/rg2/DeepFluoroLabeling-IPCAI2020) dataset, please cite:
+
+    @article{grupp2020automatic,
+      title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration},
+      author={Grupp, Robert B and Unberath, Mathias and Gao, Cong and Hegeman, Rachel A and Murphy, Ryan J and Alexander, Clayton P and Otake, Yoshito and McArthur, Benjamin A and Armand, Mehran and Taylor, Russell H},
+      journal={International journal of computer assisted radiology and surgery},
+      volume={15},
+      pages={759--769},
+      year={2020},
+      publisher={Springer}
+    }
+
+If you use the [`Ljubljana`](https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/) dataset, please cite:
+
+    @article{pernus20133d,
+      title={3D-2D registration of cerebral angiograms: A method and evaluation on clinical images},
+      author={Mitrović, Uros˘ and S˘piclin, Z˘iga and Likar, Bos˘tjan and Pernus˘, Franjo},
+      journal={IEEE transactions on medical imaging},
+      volume={32},
+      number={8},
+      pages={1550--1563},
+      year={2013},
+      publisher={IEEE}
+    }
+
+
+#### Logging
+
+We use `wandb` to log experiments. To use this feature, set the `WANDB_API_KEY` environment variable by adding the following line to your `.zshrc` or `.bashrc` file:
+
+```zsh
+export WANDB_API_KEY=your_api_key
+```