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| license: gpl-3.0 |
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| ## Model Description |
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| ### Keras Implementation of Convolutional autoencoder for image denoising |
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| This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise. |
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| Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder |
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| Keras Example Link:- https://keras.io/examples/vision/autoencoder/ |
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| ## Intended uses & limitations |
| - The trained model can be used to remove noise from any grayscale image. |
| - Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28. |
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| ## Training and evaluation data |
| - Original mnist train & test dataset were loaded from tensorflow datasets. |
| - Then Some noise was added to train & test images. |
| - Noisy images were used as input images and original clean images were used as output images for training. |
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| ## Training procedure |
| ### Training hyperparameter |
| The following hyperparameters were used during training: |
| - optimizer: 'adam' |
| - loss: 'binary_crossentropy' |
| - epochs: 100 |
| - batch_size: 128 |
| - ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function. |
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| ## Model Plot |
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| <details> |
| <summary>View Model Plot</summary> |
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|  |
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| </details> |
