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}
}