├── 3dshapes.gif ├── 3dshapes_loading_example.ipynb ├── CONTRIBUTING.md ├── LICENSE └── README.md /3dshapes.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/google-deepmind/3d-shapes/de26b40d35330adc15816ccfaa516361f789d76b/3dshapes.gif -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # How to Contribute 2 | 3 | We'd love to accept your patches and contributions to this project. There are 4 | just a few small guidelines you need to follow. 5 | 6 | ## Contributor License Agreement 7 | 8 | Contributions to this project must be accompanied by a Contributor License 9 | Agreement. You (or your employer) retain the copyright to your contribution, 10 | this simply gives us permission to use and redistribute your contributions as 11 | part of the project. 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"Disentangling by Factorising." In *Proceedings of the 35th International Conference on Machine Learning (ICML). 2018.*](http://proceedings.mlr.press/v80/kim18b.html) 5 | to assess the disentanglement properties of unsupervised learning methods. 6 | 7 | If you use this dataset in your work, please cite it as follows: 8 | ## Bibtex 9 | 10 | ``` 11 | @misc{3dshapes18, 12 | title={3D Shapes Dataset}, 13 | author={Burgess, Chris and Kim, Hyunjik}, 14 | howpublished={https://github.com/deepmind/3dshapes-dataset/}, 15 | year={2018} 16 | } 17 | ``` 18 | 19 | ## Description 20 | 21 | ![3dshapes_gif](3dshapes.gif) 22 | 23 | 3dshapes is a dataset of 3D shapes procedurally generated from 6 ground truth 24 | independent latent factors. These factors are *floor colour*, *wall colour*, *object colour*, 25 | *scale*, *shape* and *orientation*. 26 | 27 | All possible combinations of these latents are present exactly once, generating N = 480000 total images. 28 | 29 | ### Latent factor values 30 | 31 | * floor hue: 10 values linearly spaced in [0, 1] 32 | * wall hue: 10 values linearly spaced in [0, 1] 33 | * object hue: 10 values linearly spaced in [0, 1] 34 | * scale: 8 values linearly spaced in [0, 1] 35 | * shape: 4 values in [0, 1, 2, 3] 36 | * orientation: 15 values linearly spaced in [-30, 30] 37 | 38 | We varied one latent at a time (starting from orientation, then shape, etc), and sequentially stored the images in fixed order in the `images` array. The corresponding values of the factors are stored in the same order in the `labels` array. 39 | 40 | The data is stored in a HDF5 file with the following fields: 41 | 42 | * `images`: (480000 x 64 x 64 x 3, uint8) RGB images. 43 | * `labels`: (480000 x 6, float64) Values of the latent factors. 44 | 45 | ## Download data file from Google Cloud Storage 46 | You will first need to download the data from [Google Cloud Storage](https://console.cloud.google.com/storage/browser/3d-shapes). Click on this link and left-click the file `3dshapes.h5` to download it. 47 | 48 | ## Demo Jupyter Notebook 49 | 50 | The repository includes a demo that shows how to load the dataset. You can open 51 | the `.ipynb` file using [Jupyter notebook](http://jupyter.org/install.html), 52 | with Python 2 installed. Make sure that the data file `3dshapes.h5` is located 53 | at the directory where the notebook is running. 54 | 55 | 56 | ## Disclaimers 57 | 58 | This dataset was created using the [Generative Query Networks Mujoco 59 | environment](https://github.com/deepmind/gqn-datasets). 60 | This is not an official Google product. 61 | 62 | 63 | --------------------------------------------------------------------------------