Comments (1)
Hello, the experiments branch is a work in progress. You can adjust the args in the config to match what you have described. And the discriminator takes in the low-res images + the real/fake image as input.
We did not pretrain a PSNR-oriented model like in real-esrgan. We just trained the whole model with all losses that are specified in the config files.
And yes, we used imagery from the PROBA-V challenge.
from satlas-super-resolution.
Related Issues (20)
- SSRDataset is not found! HOT 1
- Problem running probav_esrgan.yml HOT 1
- About logging HOT 4
- MuS2 result HOT 8
- Inference HOT 9
- Multi-gpu training HOT 9
- Continuing the training where it stopped HOT 2
- PROBA-V dataset question HOT 3
- Meaning behind the Training data's folder structure HOT 4
- Question regarding logging in wandb HOT 2
- Dear Professor, I hope this email finds you well. My name is HATIM OUDAHA and I am a final year student at IAV MOROCCO working on my end-of-studies project. The objective of my project has been remote sensing image super-resolution. In this regard, I am writing to kindly request your assistance. Given your expertise, I believe your guidance would be invaluable in helping me if you have a pretrained model, analyze the results, and draw appropriate conclusions their performance in wich area (agriculter, urban…). if you could share with me a collab to test your model in a sentinel2 moroccan dataset. I wanna use transfert learning on it if you could explain me the Steps to have a super resolution result I understand you must have many demands on your time. However, any support you could provide would be a tremendous help for the completion of my studies. Please let me know if you could help me in this study. Regards, HOT 1
- Training time HOT 5
- Registry question HOT 2
- Inference Results - Strong Hallucinations in Urban Areas HOT 13
- How to Process Raw Sentinel-2 Data HOT 1
- Adversarial loss for ESRGAN HOT 1
- Possible Inappropriate Implementation in s2-naip_dataset.py (Reshape Issue) HOT 1
- Using 4 bands (10m) HOT 5
- Testing on small_val_set gives same super res for all input images HOT 5
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from satlas-super-resolution.