Keras
English
image-restoration
deblurring
sharpening
scratch-removal
deep-learning
computer-vision
tensorflow
Instructions to use istevit/ReviveAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use istevit/ReviveAI with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://istevit/ReviveAI") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- a8cab05f93908f4a0efab71f87a06a4c428dc5459bc727ab87a63b3e035c61af
- Size of remote file:
- 362 kB
- SHA256:
- 73fcb35571603dad0e1b47a2f498de35ed9af1f9f7acc9ec203d724c5a0dc8e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.