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Good for
Its like 2xLexicaRRDBNet model, but trained for some more with l1_gt_usm and percep_gt_usm set to true, resulting in sharper outputs. I provide both so they can be chosen based on preferrence of the user.
Training details (7)
- Date
- 2023-06-01
- Dataset
- lexica-aperture-v3-small
- Dataset size
- 43856
- Training iterations
- 220000
- Training epochs
- 18
- Training batch size
- 4
- Training HR size
- 128
Model
Rights
CC-BY-4.0Disclaimer
Private use
Commercial use
Distribution
Modifications
Credit required
State Changes
No Liability & Warranty

