| ## MPI3D Disentanglement Datasets | |
| <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/platform.jpg" width="418" height="280" /> <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/platform2.jpg" width="418" height="281" /> | |
| MPI3D datasets have been introduced to benchmark representations learning algorithms across simulated and real-world environments. The first transfer learning results of unsupervised disentangled representations are presented in our [NeurIPS 2019 paper.](https://proceedings.neurips.cc/paper/2019/hash/d97d404b6119214e4a7018391195240a-Abstract.html) | |
| --------------------------------- | |
| *UPDATE:* The download links have been updated. | |
| --------------------------------- | |
| The dataset is also used in the [NeurIPS Disentanglement Challenge.](http://www.disentanglement-challenge.com) | |
| If you use this dataset in your work then kindly cite us. | |
| ``` | |
| @inproceedings{NEURIPS2019_d97d404b, | |
| author = {Gondal, Muhammad Waleed and Wuthrich, Manuel and Miladinovic, Djordje and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch\"{o}lkopf, Bernhard and Bauer, Stefan}, | |
| booktitle = {Advances in Neural Information Processing Systems}, | |
| editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, | |
| pages = {}, | |
| publisher = {Curran Associates, Inc.}, | |
| title = {On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset}, | |
| url = {https://proceedings.neurips.cc/paper/2019/file/d97d404b6119214e4a7018391195240a-Paper.pdf}, | |
| volume = {32}, | |
| year = {2019} | |
| } | |
| ``` | |
| ## Datasets Information | |
| There are following four different datasets. The gifs are created using [disentanglement_lib](https://github.com/google-research/disentanglement_lib) visualization tool. | |
| ### 1. Real world simple shapes (mpi3d_real). | |
| <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/real1.gif"/> <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/real2.gif" /> | |
| ### 2. Realistic rendered images (mpi3d_realistic). | |
| <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/realistic1.gif" /> <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/realistic2.gif" /> | |
| ### 3. Simplistic rendered images (mpi3d_toy). | |
| <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/toy1.gif" /><img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/toy2.gif" /> | |
| ### 4. Complex real world shapes (mpi3d_complex). | |
| <img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/complex1.gif" /><img src="https://github.com/rr-learning/disentanglement_dataset/blob/master/sample_gifs/complex2.gif" /> | |
| The **first three datasets** consists of 1,036,800 images, corresponding to all the possible combinations of the following factors of variation: | |
| |Factors|Possible Values| | |
| |---|---| | |
| |object_color|white=0, green=1, red=2, blue=3, brown=4, olive=5| | |
| |object_shape|cone=0, cube=1, cylinder=2, hexagonal=3, pyramid=4, sphere=5| | |
| |object_size|small=0, large=1| | |
| |camera_height|top=0, center=1, bottom=2| | |
| |background_color|purple=0, sea green=1, salmon=2| | |
| |horizontal_axis|0,...,39| | |
| |vertical_axis|0,...,39| | |
| The **real-world complex dataset** consists of 460,800 images, containing the combinations of the following factors of variations. | |
| |Factors|Possible Values| | |
| |---|---| | |
| |object_color|yellow=0, green=1, olive=2, red=3| | |
| |object_shape|coffee-cup=0, tennis-ball=1, croissant=2, beer-cup=3| | |
| |object_size|small=0, large=1| | |
| |camera_height|top=0, center=1, bottom=2| | |
| |background_color|purple=0, sea green=1, salmon=2| | |
| |horizontal_axis|0,...,39| | |
| |vertical_axis|0,...,39| | |
| So far we only provide the datasets in 64x64 resolution. Higher resolution versions will be made available in the near future. | |
| ## Reading the Datasets | |
| The datasets are provided in the form of numpy arrays. Once the data is loaded, you can use array.reshape([6,6,2,3,3,40,40,64,64,3]) to obtain an array where the first 7 dimensions corresponds to data generative factors as in the table above and the last three to the image dimensions. Note that for real-world complex dataset you need to use array.reshape([4,4,2,3,3,40,40,64,64,3]). | |
| ``` | |
| import numpy as np | |
| data = np.load('./mpi3d_real.npz')['images'] | |
| # To visualize each factor of the data independently, you can reshape | |
| # the array as the following. | |
| data = data.reshape([6,6,2,3,3,40,40,64,64,3]) | |
| # For real-world complex dataset use: | |
| data = data.reshape([4,4,2,3,3,40,40,64,64,3]) | |
| ``` | |
| ## Downloads | |
| Use the following links to download the datasets. | |
| 1. mpi3d_toy (simplistic rendered): [link](https://huggingface.co/datasets/waleedgondal/mpi3d/resolve/main/mpi3d_toy.npz) | |
| 2. mpi3d_realistic (realistic rendered): [link](https://huggingface.co/datasets/waleedgondal/mpi3d/resolve/main/mpi3d_realistic.npz) | |
| 3. mpi3d_real (real-world images): [link](https://huggingface.co/datasets/waleedgondal/mpi3d/resolve/main/mpi3d_real.npz) | |
| 4. mpi3d_real_complex (real-world complex shapes images) : [link](https://drive.google.com/file/d/1Tp8eTdHxgUMtsZv5uAoYAbJR1BOa_OQm/view?usp=sharing) | |
| ## Feedback | |
| Please send any feedback to waleed.gondal10@gmail.com | |
| ## License | |
| This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). | |