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license: apache-2.0
task_categories:
- image-feature-extraction
- image-classification
- image-to-3d
- image-segmentation
size_categories:
- 1M<n<10M
---
## Dataset Details
This dataset derives from [Coil100](https://huggingface.co/datasets/Voxel51/COIL-100).
There are more than 1,1M images of 100 objects. Each object was turned on a turnable through 360 degrees to vary object pose with respect to a fixed color camera. Images of the objects were taken at pose intervals of 5 degrees. This corresponds to 72 poses per object.
*In addition* to the original dataset, planar rotation (9 angles) and 18 scaling factors have been applied so that there are no dependencies between factors.
Objects have a wide variety of complex geometric and reflectance characteristics.
This augmented version of Coil100 has been designed especially for Disentangled Representation Learning for real images, the Factors of Variations are:
| Factors | # values |
|----------|----------|
| Object | 100 |
| 3D Pose | 72 |
| Rotation | 9 |
| Scale | 18 |
The binarized version is also available.
## How to download
With Python > 3.0 install
```
pip install huggingface_hub
```
Then to download the RGB dataset run
```
python download_coil100.py
```
And if you want the binary version run
```
python download_coil100_binary.py
```
## Citation
if you use the dataset, please cite us:
**BibTeX:**
```bibtex
@inproceedings{NEURIPS2024_26d01e5e,
author = {Dapueto, Jacopo and Noceti, Nicoletta and Odone, Francesca},
booktitle = {Advances in Neural Information Processing Systems},
editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
pages = {21912--21948},
publisher = {Curran Associates, Inc.},
title = {Transferring disentangled representations: bridging the gap between synthetic and real images},
url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/26d01e5ed42d8dcedd6aa0e3e99cffc4-Paper-Conference.pdf},
volume = {37},
year = {2024}
}
```
## Uses
This dataset is intended for non-commercial research purposes only.
## Dataset Card Authors
[Jacopo Dapueto](https://huggingface.co/dappu97) |