Improve dataset card: Add task category, correct paper/code links, and expand usage

README.md

@@ -1,10 +1,13 @@
 ---
 license: cc-by-4.0
+task_categories:
+- GRAPH_MACHINE_LEARNING
 ---
+
 # [Neurips 2025 DB] PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding
 Official dataset release for _PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding_.
 
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white)](https://authoritywang.github.io/partnext/)
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2510.20155)
 [![Project Page](https://img.shields.io/badge/Project_Page-Website-green?logo=homepage&logoColor=white)](https://authoritywang.github.io/partnext/)
 [![🤗 Hugging Face Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/AuWang/PartNeXt)
 
@@ -14,14 +17,55 @@ Official dataset release for _PartNeXt: A Next-Generation Dataset for Fine-Grain
 
 **Neurips 2025 Dataset and Benchmark Track**
 
-| [Project Page](https://authoritywang.github.io/partnext/) | [Paper]() | [Dataset](https://huggingface.co/datasets/AuWang/PartNeXt) | [Dataset Toolkit](https://github.com/AuthorityWang/PartNeXt) | [Benchmark code (Soon)]() | [Annotation code (Soon)]() |<br>
+| [Project Page](https://authoritywang.github.io/partnext/) | [Paper](https://huggingface.co/papers/2510.20155) | [Code](https://github.com/AuthorityWang/PartNeXt) | [Dataset](https://huggingface.co/datasets/AuWang/PartNeXt) | [Dataset Toolkit](https://github.com/AuthorityWang/PartNeXt_lib) | [Benchmark code (Soon)]() | [Annotation code (Soon)]() |<br>
+
+## Sample Usage
+
+Here's how to get started with the PartNeXt dataset:
+
+### Toolkit Installation
 
-## Usage
+You can install the PartNeXt dataset toolkit from PyPI:
+```bash
+pip install partnext
+```
+If you want to install the toolkit from source or refer to the code, you can clone the [toolkit repo](https://github.com/AuthorityWang/PartNeXt_lib.git):
+```bash
+git clone https://github.com/AuthorityWang/PartNeXt_lib.git
+cd PartNeXt_lib
+pip install -e .
+```
 
-For usage, please refer to our dataset toolkit repo [https://github.com/AuthorityWang/PartNeXt](https://github.com/AuthorityWang/PartNeXt)
+### Download the dataset
+
+Our PartNeXt dataset contains two parts: 3D meshes in `.glb` format, and 3D part and hierarchy annotations. You can download them using `huggingface-cli`:
+```bash
+hf download --repo-type dataset AuWang/PartNeXt_mesh --local-dir /your/own/path/PartNeXt_mesh
+hf download --repo-type dataset AuWang/PartNeXt --local-dir /your/own/path/PartNeXt_annotations
+```
+
+### Load the dataset
+
+You can load the PartNeXt annotations using the Hugging Face `datasets` library:
+```python
+from datasets import load_dataset
+
+# Load the annotation dataset
+dataset = load_dataset("AuWang/PartNeXt")
+
+# Access a sample from the 'train' split (or 'validation', 'test' if available)
+print(dataset["train"][0])
+
+# To use the PartNeXt dataset toolkit, import it:
+import partnext
+
+# For full usage examples, refer to the toolkit repository:
+# https://github.com/AuthorityWang/PartNeXt_lib
+# Specifically, check 'example/toolkit_example.py' for detailed usage.
+```
 
 ## Acknowledgement
-Our PartNeXt dataset is based on [Objaverse](https://objaverse.allenai.org/), [ABO](https://amazon-berkeley-objects.s3.amazonaws.com/index.html), [3D-Future](https://tianchi.aliyun.com/dataset/98063), thanks for these awesome datasets. If there is any license issue, please contact us and we will remove the data. 
+Our PartNeXt dataset is based on [Objaverse](https://objaverse.allenai.org/), [ABO](https://amazon-berkeley-objects.s3.amazonaws.com/), [3D-Future](https://tianchi.aliyun.com/dataset/98063), thanks for these awesome datasets. If there is any license issue, please contact us and we will remove the data. 
 
 Thanks for Benyuan AI data for data annotation.