Add dataset card

README.md

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+---
+license: mit
+task_categories:
+- image-classification
+tags:
+- 3d
+- point-cloud
+- mesh
+- modelnet40
+- cad
+pretty_name: ModelNet40 Auto Aligned
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+dataset_info:
+  features:
+  - name: object_id
+    dtype: large_string
+  - name: class
+    dtype: large_string
+  - name: split
+    dtype: large_string
+  - name: object_path
+    dtype: large_string
+  - name: __index_level_0__
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 186438
+    num_examples: 9843
+  - name: test
+    num_bytes: 48215
+    num_examples: 2468
+  download_size: 234653
+  dataset_size: 234653
+---
+
+# ModelNet40 Auto Aligned
+
+Auto-aligned version of the [ModelNet40](https://modelnet.cs.princeton.edu/) 3D CAD dataset. Each sample is an OFF mesh file organized by class and train/test split.
+
+This dataset mirrors the layout of [`naderalfares/ModelNet40`](https://huggingface.co/datasets/naderalfares/ModelNet40), but uses the auto-aligned meshes from the Princeton ModelNet release.
+
+## Dataset structure
+
+```
+modelnet40_auto_aligned/
+  {class}/
+    train/{class}_{id}.off
+    test/{class}_{id}.off
+```
+
+- **40 classes** (airplane, bathtub, bed, …, xbox)
+- **9,843** training meshes
+- **2,468** test meshes
+- **12,311** meshes total (~9.7 GB)
+
+The parquet manifest stores metadata only. Mesh files live under `modelnet40_auto_aligned/` and are referenced by the `object_path` column (without that prefix).
+
+## Load metadata with 🤗 Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("naderalfares/ModelNet40_Auto_aligned")
+
+print(ds)
+# DatasetDict({
+#     train: Dataset({ features: ['object_id', 'class', 'split', 'object_path', '__index_level_0__'], num_rows: 9843 })
+#     test:  Dataset({ features: ['object_id', 'class', 'split', 'object_path', '__index_level_0__'], num_rows: 2468 })
+# })
+
+row = ds["train"][0]
+print(row)
+# {'object_id': 'airplane_0001', 'class': 'airplane', 'split': 'train',
+#  'object_path': 'airplane/train/airplane_0001.off', '__index_level_0__': 100}
+```
+
+## Download a mesh file
+
+```python
+from huggingface_hub import hf_hub_download
+
+repo_id = "naderalfares/ModelNet40_Auto_aligned"
+row = ds["train"][0]
+
+mesh_path = hf_hub_download(
+    repo_id=repo_id,
+    repo_type="dataset",
+    filename=f"modelnet40_auto_aligned/{row['object_path']}",
+)
+print(mesh_path)  # local path to airplane_0001.off
+```
+
+## Citation
+
+If you use this dataset, please cite the original ModelNet paper and the auto-alignment work:
+
+```bibtex
+@inproceedings{wu20153d,
+  title={3D ShapeNets: A Deep Representation for Volumetric Shapes},
+  author={Wu, Zhirong and Song, Shuran and Khademi, Adarsh and Zhao, Tian and others},
+  booktitle={CVPR},
+  year={2015}
+}
+```
+
+## License
+
+MIT