Instructions to use keras-io/super-resolution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-io/super-resolution with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("keras-io/super-resolution") - Notebooks
- Google Colab
- Kaggle
Notes
- This model is a trained version of the Keras Tutorial Image Super Resolution
- The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.
Link to a pyimagesearch tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net.
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