| | --- |
| | dataset_info: |
| | features: |
| | - name: image_lt |
| | dtype: image |
| | - name: image_rt |
| | dtype: image |
| | - name: category |
| | dtype: int32 |
| | - name: instance |
| | dtype: int32 |
| | - name: elevation |
| | dtype: int32 |
| | - name: azimuth |
| | dtype: int32 |
| | - name: lighting |
| | dtype: int32 |
| | splits: |
| | - name: train |
| | num_bytes: 117947794.0 |
| | num_examples: 24300 |
| | - name: test |
| | num_bytes: 118130266.0 |
| | num_examples: 24300 |
| | download_size: 236815224 |
| | dataset_size: 236078060.0 |
| | --- |
| | # Dataset Card for "smallnorb" |
| |
|
| | ## Table of Contents |
| | - [Table of Contents](#table-of-contents) |
| | - [Dataset Description](#dataset-description) |
| | - [Dataset Summary](#dataset-summary) |
| | - [Dataset Structure](#dataset-structure) |
| | - [Data Instances](#data-instances) |
| | - [Data Fields](#data-fields) |
| | - [Data Splits](#data-splits) |
| | - [Additional Information](#additional-information) |
| | - [Dataset Curators](#dataset-curators) |
| | - [Licensing Information](#licensing-information) |
| | - [Citation Information](#citation-information) |
| | - [Contributions](#contributions) |
| |
|
| | ## Dataset Description |
| |
|
| | **NOTE:** This dataset is an unofficial port of small NORB based on a [repo from Andrea Palazzi](https://github.com/ndrplz/small_norb) using this [script](https://colab.research.google.com/drive/1Tx20uP1PrnyarsNCWf1dN9EQyr38BDIE?usp=sharing). For complete and accurate information, we highly recommend visiting the dataset's original homepage. |
| |
|
| | - **Homepage:** https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/ |
| | - **Paper:** https://ieeexplore.ieee.org/document/1315150 |
| |
|
| | ### Dataset Summary |
| |
|
| | From the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/): |
| |
|
| | > This database is intended for experiments in 3D object reocgnition from shape. It contains images of 50 toys belonging to 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. The objects were imaged by two cameras under 6 lighting conditions, 9 elevations (30 to 70 degrees every 5 degrees), and 18 azimuths (0 to 340 every 20 degrees). |
| | > |
| | > The training set is composed of 5 instances of each category (instances 4, 6, 7, 8 and 9), and the test set of the remaining 5 instances (instances 0, 1, 2, 3, and 5). |
| |
|
| | ## Dataset Structure |
| |
|
| | ### Data Instances |
| |
|
| | An example of an instance in this dataset: |
| |
|
| | ``` |
| | { |
| | 'image_lt': <PIL.PngImagePlugin.PngImageFile image mode=L size=96x96 at 0x...>, |
| | 'image_rt': <PIL.PngImagePlugin.PngImageFile image mode=L size=96x96 at 0x...>, |
| | 'category': 0, |
| | 'instance': 8, |
| | 'elevation': 6, |
| | 'azimuth': 4, |
| | 'lighting': 4 |
| | } |
| | ``` |
| |
|
| | ### Data Fields |
| |
|
| | Explanation of this dataset's fields: |
| |
|
| | - `image_lt`: a PIL image of an object from the dataset taken with one of two cameras |
| | - `image_rt`: a PIL image of an object from the dataset taken with one of two cameras |
| | - `category`: the category of the object shown in the images |
| | - `instance`: the instance of the category of the object shown in the images |
| | - `elevation`: the label of the elevation of the cameras used in capturing a picture of the object |
| | - `azimuth`: the label of the azimuth of the cameras used in capturing a picture of the object |
| | - `lighting`: the label of the lighting condition used in capturing a picture of the object |
| |
|
| | For more information on what these categories and labels pertain to, please see [Dataset Summary](#dataset-summary) or the [repo](https://github.com/ndrplz/small_norb) used in processing the dataset. |
| |
|
| | ### Data Splits |
| |
|
| | Information on this dataset's splits: |
| |
|
| | | | train | test | |
| | |------|------:|------:| |
| | | size | 24300 | 24300 | |
| |
|
| | ## Additional Information |
| |
|
| | ### Dataset Curators |
| |
|
| | Credits from the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/): |
| |
|
| | > [Fu Jie Huang](http://www.cs.nyu.edu/jhuangfu/), [Yann LeCun](http://yann.lecun.com/) |
| | > |
| | > Courant Institute, New York University |
| | > |
| | > October, 2005 |
| |
|
| | ### Licensing Information |
| |
|
| | From the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/): |
| |
|
| | > This database is provided for research purposes. It cannot be sold. Publications that include results obtained with this database should reference the following paper: |
| | > |
| | > Y. LeCun, F.J. Huang, L. Bottou, Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2004 |
| |
|
| | ### Citation Information |
| |
|
| | From the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/): |
| |
|
| | > Publications that include results obtained with this database should reference the following paper: |
| | > |
| | > Y. LeCun, F.J. Huang, L. Bottou, Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2004 |
| |
|
| | ``` |
| | @inproceedings{lecun2004learning, |
| | title={Learning methods for generic object recognition with invariance to pose and lighting}, |
| | author={LeCun, Yann and Huang, Fu Jie and Bottou, Leon}, |
| | booktitle={Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.}, |
| | volume={2}, |
| | pages={II--104}, |
| | year={2004}, |
| | organization={IEEE} |
| | } |
| | ``` |
| |
|
| | DOI: [10.1109/CVPR.2004.1315150](https://doi.org/10.1109/CVPR.2004.1315150) |
| |
|
| | ### Contributions |
| |
|
| | Code to process small NORB adapted from [Andrea Palazzi's repo](https://github.com/ndrplz/small_norb) with this [script](https://colab.research.google.com/drive/1Tx20uP1PrnyarsNCWf1dN9EQyr38BDIE?usp=sharing). |
