Improve model card metadata and links

README.md

@@ -1,21 +1,25 @@
----
-license: mit
-datasets:
-- frallebini/gmnerf
-tags:
-- weight-space-learning
-- nerf
-- graph-metanetwork
----
+---
+datasets:
+- frallebini/gmnerf
+license: mit
+pipeline_tag: feature-extraction
+tags:
+- weight-space-learning
+- nerf
+- graph-metanetwork
+---
 
 # Weight Space Representation Learning on Diverse NeRF Architectures (ICLR 2026)
 
 [![paper](https://img.shields.io/badge/arxiv-paper-darkred?logo=arxiv)](https://arxiv.org/abs/2502.09623)
 [![code](https://img.shields.io/badge/github-code-blue?logo=github)](https://github.com/CVLAB-Unibo/gmnerf)
+[![project](https://img.shields.io/badge/project-page-green)](https://cvlab-unibo.github.io/gmnerf)
 [![datasets](https://img.shields.io/badge/huggingface-datasets-teal?logo=huggingface)](https://huggingface.co/datasets/frallebini/gmnerf)
 
 ![teaser](https://cvlab-unibo.github.io/gmnerf/static/images/teaser.svg)
 
+This repository contains the models and artifacts for **GM-NeRF**, a framework capable of processing Neural Radiance Fields (NeRFs) with diverse architectures (MLPs, tri-planes, and hash tables) and performing inference on architectures unseen at training time. This is achieved via a Graph Meta-Network trained within an unsupervised representation learning framework.
+
 ## Repo content
 
 - `l_con/best.pt`: \\(\mathcal{L}_\text{C}\\) model
@@ -23,6 +27,8 @@ tags:
 - `l_rec_con/best.pt`: \\(\mathcal{L}_\text{R+C}\\) model
 - `llana`: [LLaNA](https://andreamaduzzi.github.io/llana) model trained on \\(\mathcal{L}_\text{R+C}\\) embeddings
 
+For installation and usage instructions (including graph and embedding computation), please refer to the [official GitHub repository](https://github.com/CVLAB-Unibo/gmnerf).
+
 ## Cite us
 
 If you find our work useful, please cite us:
@@ -33,4 +39,5 @@ If you find our work useful, please cite us:
   author = {Ballerini, Francesco and Zama Ramirez, Pierluigi and Di Stefano, Luigi and Salti, Samuele},
   booktitle = {The Fourteenth International Conference on Learning Representations},
   year = {2026}
+}
 ```