Download and resources

Note

This dataset is released under the Creative Commons Attribution 4.0 International License.

Note

When using the IntPhys dataset for publications, please cite our paper on arxiv

Datasets

Click on the links below to download the intphys datasets.

Train dataset includes 15k scenes with full metadata.
Test datasets include 1080 scenes for each block without metadata.
Development dataset includes 30 scenes for each block with partial metadata (physical plausibility of the scene is given).
The entire dataset has a size of about 250G.

File	Description	Size	MD5 sum
starting_kit.zip	Challenge starting kit	180K	`cf699d546892d206c51ceff352a2534c`
dev.tar.gz	Development data (all blocks)	3G	`6a72715007f2f3b0e9546bfa8d8fc39b`
test.O1.tar.gz	Test data (block O1)	36G	`cc998a7a1b14bba988bd1ef31ae6cd6b`
test.O2.tar.gz	Test data (block O2)	36G	`d6d8475bfd06298e938b8925ad326e7e`
test.O3.tar.gz	Test data (block O3)	36G	`318445e082357e1256eff63a96348c8b`
train.1.tar.gz	Train data (1/4)	35G	`3428bcf398441a489111528634530212`
train.2.tar.gz	Train data (2/4)	35G	`47dc1c3578e079584f4e3b5b8ab8304b`
train.3.tar.gz	Train data (3/4)	35G	`389782958ad058f7a7e1db2b07748259`
train.4.tar.gz	Train data (4/4)	35G	`7d345eaa3a33d2abc4a75442eefb277a`

Additional resources

Download the paper at https://arxiv.org/abs/1803.07616.
Source code for data generation (train videos only) is available at https://github.com/bootphon/intphys. The code for test videos generation is kept private.
Source code for baselines at https://github.com/rronan/IntPhys-Baselines.
BibTex citation of the reference paper for the dataset:

@article{DBLP:journals/corr/abs-1803-07616,
   author    = {Ronan Riochet and
                Mario Ynocente Castro and
                Mathieu Bernard and
                Adam Lerer and
                Rob Fergus and
                V{\'{e}}ronique Izard and
                Emmanuel Dupoux},
   title     = {IntPhys: {A} Framework and Benchmark for Visual Intuitive Physics
                Reasoning},
   journal   = {CoRR},
   volume    = {abs/1803.07616},
   year      = {2018},
   url       = {http://arxiv.org/abs/1803.07616},
   archivePrefix = {arXiv},
   eprint    = {1803.07616},
   timestamp = {Mon, 13 Aug 2018 16:47:10 +0200},
   biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1803-07616},
   bibsource = {dblp computer science bibliography, https://dblp.org}
 }