license: other
license_name: bsd-3-clause-pytorch-tutorials
license_link: https://github.com/pytorch/tutorials/blob/main/LICENSE
tags:
- kornia
- super-resolution
kornia/small_sr
Pretrained weights for the small sub-pixel super-resolution network used by kornia.models.small_sr.SmallSRNet (upscale_factor=3).
The network upscales a single-channel (luminance) image with an efficient sub-pixel convolution layer (Shi et al., CVPR 2016). It is the model from PyTorch's super-resolution tutorial.
Weights
| File | Description |
|---|---|
superres_epoch100-44c6958e.safetensors |
3x upscaling, the tutorial's checkpoint |
Provenance
superres_epoch100-44c6958e.safetensors holds the state dict of PyTorch's
superres_epoch100-44c6958e.pth
(sha256 44c6958ea9df3886ef120bad1fc827ffb913e4e6f667b24dc7aea9b032b6df75), unchanged: the same keys, dtypes,
shapes and bits. The kornia maintainers converted it (converter revision 0c3db846b2) and checked that a model
loaded from either file gives bitwise-identical outputs. The sha256 of the converted file is
84d6ae56ac55d03df3089b42bfc0dc6786b5ebb48a44d522bdc68d66d85cde8c.
License
The weights come from the PyTorch tutorials, whose repository is licensed under BSD-3-Clause; see
LICENSE, copied from pytorch/tutorials. No separate licence is
stated for the weights file itself.
Citation
@inproceedings{shi2016real,
author = {Shi, Wenzhe and Caballero, Jose and Husz{\'a}r, Ferenc and Totz, Johannes and Aitken, Andrew P.
and Bishop, Rob and Rueckert, Daniel and Wang, Zehan},
title = {Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
Neural Network},
booktitle = {CVPR},
year = {2016}
}