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README.md
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- image-feature-extraction
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- image-classification
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- image-to-3d
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size_categories:
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- 1M<n<10M
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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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## Citation
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**BibTeX:**
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```bibtex
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## Dataset Card Authors
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[Jacopo Dapueto](https://huggingface.co/dappu97)
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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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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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## Dataset Card Authors
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[Jacopo Dapueto](https://huggingface.co/dappu97)
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