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--- |
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license: mit |
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tags: |
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- image-to-image |
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datasets: |
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- fka/awesome-chatgpt-prompts |
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language: |
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- ab |
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metrics: |
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- accuracy |
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base_model: |
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- stabilityai/stable-diffusion-3.5-large |
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new_version: microsoft/OmniParser |
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pipeline_tag: translation |
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library_name: adapter-transformers |
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--- |
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## Notes |
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* This model is a trained version of the Keras Tutorial [Image Super Resolution](https://keras.io/examples/vision/super_resolution_sub_pixel/) |
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* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300. |
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[Link to a pyimagesearch](https://www.pyimagesearch.com/2021/09/27/pixel-shuffle-super-resolution-with-tensorflow-keras-and-deep-learning/) tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net. |
