This repository contains our submission to the ML Challenge organized by the Stanford FLAME AI 2023 Workshop.
The challenge's objective is to perform super-resolution on low-resolution 2D flowfield images to reconstruct high-resolution versions of these flowfields.
This code is written in python, and needs pytorch and torchvision to run. It is optimized for GPU usage.
- Clone this repository
- Copy the data from the challenge to the
data
folder. The folder structure should be as follows:
data
├──dataset
├── train.csv
├── test.csv
├── val.csv
├── flowfields
│ ├── ...
- Download the models checkpoints into the
checkpoints
folder. The folder structure should be as follows:
checkpoints
├── model_1.pth
├── ...
- Launch jupyter lab
- Open
loading_model.ipynb
The model achieves a L2 loss of :
- 0.00553 on the private test set
- 0.00462 on the validation set
The best individual model vxyhe45d
achieves a L2 loss of:
- 0.00600 on the private test set.
- 0.00478 on the validation set
- Thomas X Wang, ISIR, Sorbonne Université
- Louis Serrano, ISIR, Sorbonne Université
This code is based on Super-Resolution Neural Operator