Image-to-Image
Transformers
PyTorch
TensorBoard
Spanish
English
conv_swin2sr
climate
super-resolution
Instructions to use predictia/convswin2sr_mediterranean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use predictia/convswin2sr_mediterranean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="predictia/convswin2sr_mediterranean")# Load model directly from transformers import ConvSwin2SR model = ConvSwin2SR.from_pretrained("predictia/convswin2sr_mediterranean", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47a8e8ac223ee1ec027e7b8bab5c03035cb3f2808a1d4d04dc9f57a79599fc15
- Size of remote file:
- 49.8 MB
- SHA256:
- 2ed5ccc3acc68be004ea0cf728ca9cdf996de62515ffa07142728aeb658d8471
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.