|  | --- | 
					
						
						|  | 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) |