Official repository of the RAISE-LPBF-Laser benchmark.
We hereby provide PyTorch code to load the dataset as well as the models we used as baselines.
More information can be found on our website makebench.eu.
A collection of PyTorch Datasets can be found in the dataset.py
file:
FramesSP
: extracts frames for training/testing given the filepath to a RAISE-LPBF-Laser HDF5 dataset as downloaded from the website;OneWaySP
: inherits fromFramesSP
to further preprocess the frames for compatibility with video recognition models such as 3DResnet, X3D, MViT, etc.;TwoWaysSP
: inherits fromFramesSP
to further preprocess the frames for compatibility with two-way model SlowFast.
This is just a baseline to demonstrate how to use the data; doubtlessly it is possible to achieve better performance. We encourage everyone to submit results of improved models or preprocessing methods to Makebench.eu.
A collection of models as PyTorch Modules can be found in the models/
folder:
CNN3DResnet
: Hara et Al. (2017). Learning spatio-temporal features with 3D residual networks for action recognition.CNN3DSlowFast
: Feichtenhofer et Al. (2019). SlowFast Networks for Video Recognition.MViT
: Fan et Al. (2021). Multiscale Vision Transformers.Swin3D
: Yang et Al. (2023). Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding.ViViT
: Arnab et Al. (2021). ViViT: A Video Vision Transformer.X3D
: Feichtenhofer et Al. (2020). X3D: Expanding Architectures for Efficient Video Recognition.
@article{BLANC2023100161,
title = {Reference dataset and benchmark for reconstructing laser parameters from on-axis video in powder bed fusion of bulk stainless steel},
journal = {Additive Manufacturing Letters},
volume = {7},
pages = {100161},
year = {2023},
issn = {2772-3690},
doi = {https://doi.org/10.1016/j.addlet.2023.100161},
url = {https://www.sciencedirect.com/science/article/pii/S2772369023000427},
author = {Cyril Blanc and Ayyoub Ahar and Kurt {De Grave}},
keywords = {Selective laser melting, Stainless steel, On-axis camera, Dataset, Machine learning, Monitoring}
}