Instructions to use keras-io/conv_autoencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-io/conv_autoencoder with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("keras-io/conv_autoencoder") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -4,6 +4,8 @@ license: gpl-3.0
|
|
| 4 |
|
| 5 |
# Keras Implementation of Convolutional autoencoder for image denoising
|
| 6 |
|
|
|
|
|
|
|
| 7 |
Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder
|
| 8 |
|
| 9 |
Keras Example Link:- https://keras.io/examples/vision/autoencoder/
|
|
|
|
| 4 |
|
| 5 |
# Keras Implementation of Convolutional autoencoder for image denoising
|
| 6 |
|
| 7 |
+
This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.
|
| 8 |
+
|
| 9 |
Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder
|
| 10 |
|
| 11 |
Keras Example Link:- https://keras.io/examples/vision/autoencoder/
|