pdf pdf |
|---|
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Check out the documentation for more information.
EMAG2 completion
Recovering Missing Regions of Earth Magnetic Anomaly Grid data (EMAG2) Using RePaint based on Diffusion Model
Set up
1. Environment
pip install numpy torch blobfile tqdm pyYaml pillow pandas # e.g. torch 1.7.1+cu110.
2. Download pretrained model and EMAG2 data.
| Name | Note |
|---|---|
| Completion results(OneDrive) | Our completion results including csv and pdf |
| Completion results(HuggingFace) | FerrisMao/EMAG2-completion |
| Public EMAG2 data | EMAG2 data |
| Pretrained model | Pretrained guided-diffusion model |
Place the pretrained model under ./pretrain and original EMAG2 data under ./EMAG2.
3. Run example
We prepare an easy test for quick evaluation. The input images and masks are in ./data.
bash shell/easy_test.sh
Completion method
1. Step 0: Data preprocess
Download EMAG2_V3 and place it in ./EMAG2. Run the below command, you can preprocess the EMAG2_V3.
python scripts/preprocess.py
2. Step 1: Global completion
bash shell/step1.sh
3. Step 2: Local completion
bash shell/step2.sh
FAQ
How to apply it for other datasets?
If you want train new completion model on a new dataset, it is recommended to follow guided-diffusion repository to obtain guided-diffusion model, then follow our completion method.
Acknowledgements
Our code is built upon RePaint and guided-diffuion. We thank the authors for their excellent work.
If you have any question, feel free to contact fymao@zju.edu.cn or fangyuanmaocs@gmail.com .
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