Comments (2)
You will need to add pretrain_network_g and pretrain_network_d arguments to the .yml file to load in weights for both the generator and the discriminator. If you want this to happen automatically, you can specify the --auto_resume flag in the training command.
To continue logging in wandb you'll need to edit the wandb.init() code in the repo. I haven't tested this, but you'd need to set resume=True.
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Okay, thank you.
from satlas-super-resolution.
Related Issues (20)
- 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
- Can't reproduce the results on new Sentinel-2 imagery (but on the test set, it works perfectly) HOT 1
- Numpy version is not working HOT 3
- Inference on Single Image HOT 7
- 'recursive=True' might be missing in glob function (ssr/infer.py) HOT 1
- Super Resolution Issue: Visible Tile Boundaries HOT 2
- Using Satlas Super Resolution for NDVI Purposes HOT 5
- The SR3 diffusion model code repo HOT 1
- Bad results when retraining ESRGAN from scratch and retrain with pre-train weights HOT 5
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