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--- |
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license: apache-2.0 |
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task_categories: |
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- image-feature-extraction |
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- image-classification |
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- image-to-3d |
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- image-segmentation |
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size_categories: |
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- 1M<n<10M |
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--- |
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## Dataset Details |
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This dataset derives from [Coil100](https://huggingface.co/datasets/Voxel51/COIL-100). |
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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. Then planar rotation (9 angles) and 18 scaling factors has been applied. |
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Objects have a wide variety of complex geometric and reflectance characteristics. |
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This augmented version of Coil100 has been design for Disentangled Representation Learning for real images, the Factors of Variations are: |
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| Factors | # values | |
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|----------|----------| |
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| Object | 100 | |
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| 3D Pose | 72 | |
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| Rotation | 9 | |
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| Scale | 18 | |
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The binarized version is also available. |
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## How to download |
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With Python > 3.0 install |
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``` |
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pip install huggingface_hub |
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``` |
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Then to download the RGB dataset run |
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``` |
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python download_coil100.py |
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``` |
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And if you want the binary version run |
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``` |
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python download_coil100_binary.py |
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``` |
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## Citation |
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if you use the dataset, please cite us: |
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**BibTeX:** |
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```bibtex |
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@article{dapueto2024transferring, |
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title={Transferring disentangled representations: bridging the gap between synthetic and real images}, |
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author={Dapueto, Jacopo and Noceti, Nicoletta and Odone, Francesca}, |
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journal={arXiv preprint arXiv:2409.18017}, |
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year={2024} |
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} |
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``` |
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## Uses |
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This dataset is intended for non-commercial research purposes only. |
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## Dataset Card Authors |
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[Jacopo Dapueto](https://huggingface.co/dappu97) |
