Add SmallSR weights in safetensors (state dict of PyTorch's superres_epoch100-44c6958e.pth), LICENSE, card
8771f8b verified | 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](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html). | |
| ## 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`](https://s3.amazonaws.com/pytorch/test_data/export/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`](LICENSE), copied from [pytorch/tutorials](https://github.com/pytorch/tutorials). No separate licence is | |
| stated for the weights file itself. | |
| ## Citation | |
| ```bibtex | |
| @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} | |
| } | |
| ``` | |