Datasets:
File size: 2,838 Bytes
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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
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