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read_data.py Python script to read the images from a directory and make a numpy array Jun 7, 2019

README.md

MPI3D Disentanglement datasets

MPI3D datasets have been introduced as a part of NeurIPS 2019 Disentanglement Competition. There are three different datasets:

  1. Simplistic rendered images (mpi3d_toy).
  2. Realistic rendered images (mpi3d_realistic).
  3. Real world images (mpi3d_real).

Each dataset consists of 460800 images corresponding to all possible combinations of the following factors of variation:

Factors Possible Values
object_color green=0, red=1, blue=2, brown=3
object_shape cone=0, cube=1, hexagonal prism=2, sphere=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

Each image has as filename padded_index.png where
index = object_color * 115200 + object_shape * 28800 + object_size * 14400 + camera_height * 4800 + background_color * 1600 + horizontal_axis * 40 + vertical_axis
padded_index = index padded with zeros such that it has 6 digits.

If you use python, this means that once the data is loaded into a numpy array you can use array.reshape([4,4,2,3,3,40,40]) to obtain an array where each dimension corresponds to a factor. Size of images for the simplistic rendered dataset are 64x64.

For more details on the dataset please consult https://arxiv.org/abs/1906.03292. For loading the dataset you may make use of the python scripts in this repository. If you use this dataset then kindly cite us.

@article{gondal2019transfer,
  title={On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset},
  author={Gondal, Muhammad Waleed and W{\"u}thrich, Manuel and Miladinovi{\'c}, {\DJ}or{\dj}e and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch{\"o}lkopf, Bernhard and Bauer, Stefan},
  journal={arXiv preprint arXiv:1906.03292},
  year={2019}
}

Links to datasets

simplistic rendered: https://storage.cloud.google.com/disentanglement_dataset/sim_toy_ordered.tar.gz
realistic rendered: not yet published
real images: not yet published

License

This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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