Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: mit
----
+---
+license: mit
+language:
+  - en
+tags:
+  - 3d
+  - point-cloud
+  - mesh
+  - classification
+  - modelnet
+pretty_name: ModelNet10
+size_categories:
+  - 1G<n<10G
+---
+
+# ModelNet10
+
+[ModelNet10](https://modelnet.cs.princeton.edu/) is a subset of ModelNet with **10 object categories** of 3D CAD models, widely used for shape classification and point-cloud experiments. This repository packages the meshes as **OFF** files with a CSV index for use with the [Hugging Face `datasets`](https://huggingface.co/docs/datasets) library.
+
+## Dataset structure
+
+- `metadata_modelnet10.csv` — index with columns: `object_id`, `class`, `split`, `object_path`
+- `ModelNet10/` — mesh files (`.off`) organized by category and split
+- `modelnet10.py` — `GeneratorBasedBuilder` loading script
+
+Each example exposes string fields `object_id`, `class`, `split`, and `file_path` (absolute path to the `.off` file after resolution).
+
+## Usage
+
+Load from the Hub (downloads and caches on first use):
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("naderalfares/ModelNet10")
+```
+
+Load from a local directory that contains `metadata_modelnet10.csv` and the `ModelNet10/` folder:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "path/to/modelnet10.py",
+    name="default",
+    data_dir="path/to/ModelNet10",
+)
+```
+
+Access splits:
+
+```python
+train = ds["train"]
+row = train[0]
+print(row["class"], row["file_path"])
+```
+
+## Citation
+
+If you use ModelNet, please cite:
+
+```bibtex
+@inproceedings{wu20153d,
+  title={3D ShapeNets: A Deep Representation for Volumetric Shapes},
+  author={Wu, Zhirong and Song, Shuran and Khosla, Aditya and Yu, Fisher and Zhang, Linguang and Tang, Xiaoou and Xiao, Jianxiong},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={1912--1920},
+  year={2015}
+}
+```