predictia
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convswin2sr_mediterranean

super-resolution

Inference Endpoints

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jpxkqx commited on Oct 23, 2023

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 The model architecture is based on the original Swin2 arcthitecture for Super Resolution (SR) tasks. The library [transformers](https://github.com/huggingface/transformers) is used to simplify the model design.
-<img src="https://huggingface.co/predictia/convswin2sr_mediterranean/blob/main/Diagrama%20%20Swin2SR.png">
 The main component of the model is a [transformers.Swin2SRModel](https://huggingface.co/docs/transformers/model_doc/swin2sr#transformers.Swin2SRModel) which increases x8 the spatial resolution of its inputs (Swin2SR only supports upscaling ratios power of 2).
 As the real upscale ratio is ~5 and the output shape of the region considered is (160, 240), a Convolutional Neural Network (CNN) is included as a pre-process component which convert the inputs into a (40, 60) feature maps that can be fed to the Swin2SRModel.

 The model architecture is based on the original Swin2 arcthitecture for Super Resolution (SR) tasks. The library [transformers](https://github.com/huggingface/transformers) is used to simplify the model design.
+![architecture](architecture.png)
 The main component of the model is a [transformers.Swin2SRModel](https://huggingface.co/docs/transformers/model_doc/swin2sr#transformers.Swin2SRModel) which increases x8 the spatial resolution of its inputs (Swin2SR only supports upscaling ratios power of 2).
 As the real upscale ratio is ~5 and the output shape of the region considered is (160, 240), a Convolutional Neural Network (CNN) is included as a pre-process component which convert the inputs into a (40, 60) feature maps that can be fed to the Swin2SRModel.