Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022
- 1. INFN, Padua
- 2. Université catholique de Louvain
- 3. CERN
- 4. Lebanese University
- 5. Technical University of Munich
Description
Data is in HDF5 format (with LZF compression). For specifics and details, please see https://github.com/GilesStrong/mode_diffprog_22_challenge, N.B. Link active after 01/08/22
The training file contains two datasets:
- `'x0'`: a set of voxelwise X0 predictions (float32)
- `'targs'`: a set of voxelwise classes (int):
- 0 = soil
- 1 = wall
The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position).
All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels.
The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer.
It can be read using e.g. the code below:
with open('train.h5') as h5:
inputs = h5['x0'][()]
targets = h5['targs'][()]
The test file only contains the X0 inputs:
with open('test.h5') as h5:
inputs = h5['x0'][()]
Files
Additional details
Related works
- Is documented by
- Software: 10.5281/zenodo.6947862 (DOI)