Add feature-extraction task category and usage information

README.md

@@ -1,5 +1,7 @@
 ---
 license: mit
+task_categories:
+- feature-extraction
 viewer: false
 tags:
 - weight-space-learning
@@ -11,10 +13,29 @@ tags:
 
 [![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)
 [![models](https://img.shields.io/badge/huggingface-models-plum?logo=huggingface)](https://huggingface.co/frallebini/gmnerf)
 
 ![teaser](https://cvlab-unibo.github.io/gmnerf/static/images/teaser.svg)
 
+This repository contains the dataset for the paper [Weight Space Representation Learning on Diverse NeRF Architectures](https://huggingface.co/papers/2502.09623). The framework is capable of processing NeRFs with diverse architectures (MLPs, tri-planes, and hash tables) by training a Graph Meta-Network to obtain architecture-agnostic latent spaces.
+
+## Usage
+
+You can use the official scripts provided in the [GitHub repository](https://github.com/CVLAB-Unibo/gmnerf) to interact with the data.
+
+### Graph computation
+To compute the graphs of NeRFs (e.g., test set of the MLP architecture):
+```bash
+python export_graphs.py --data-root ./data --dataset shapenet --arch mlp --split test
+```
+
+### Embedding computation
+To compute embeddings produced by the trained $\mathcal{L}_{\text{R+C}}$ encoder:
+```bash
+python export_embs.py --ckpt_name l_rec_con --data.root ./data --dataset shapenet --arch mlp --split test
+```
+
 ## NeRF weights
 
 Main dataset structure:
@@ -114,15 +135,15 @@ Main dataset structure:
                     └── *.h5
 ```
 where `model`s are:
-- `l_con`, aka \\(\mathcal{L}_\text{C}\\)
-- `l_rec`, aka \\(\mathcal{L}_\text{R}\\)
-- `l_rec_con`, aka \\(\mathcal{L}_\text{R+C}\\)
+- `l_con`, aka $\mathcal{L}_\text{C}$
+- `l_rec`, aka $\mathcal{L}_\text{R}$
+- `l_rec_con`, aka $\mathcal{L}_\text{R+C}$
 
 Unseen architectures (`emb/model/shapenet/hash_unseen`, `emb/model/shapenet/mlp_unseen`, and `emb/model/shapenet/triplane_unseen`) and Objaverse NeRFs (`emb/model/objaverse`) have analogous directory structures.
 
 ## Language data
 
-The `language` directory contains \\(\mathcal{L}_\text{R+C}\\) embeddings (i.e. those found in `emb/l_rec_con/shapenet`) paired with textual annotations from the [ShapeNeRF-Text dataset](https://huggingface.co/datasets/andreamaduzzi/ShapeNeRF-Text/tree/main). This directory structure allows running the [official LLaNA code](https://github.com/CVLAB-Unibo/LLaNA) without any additional preprocessing.
+The `language` directory contains $\mathcal{L}_\text{R+C}$ embeddings (i.e. those found in `emb/l_rec_con/shapenet`) paired with textual annotations from the [ShapeNeRF-Text dataset](https://huggingface.co/datasets/andreamaduzzi/ShapeNeRF-Text/tree/main). This directory structure allows running the [official LLaNA code](https://github.com/CVLAB-Unibo/LLaNA) without any additional preprocessing.
 
 ## Cite us
 
@@ -134,4 +155,5 @@ 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}
+}
 ```