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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 |
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- The trained model can be used to remove noise from any grayscale image. |
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- 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 |
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- Original mnist train & test dataset were loaded from tensorflow datasets. |
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- Then Some noise was added to train & test images. |
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- Noisy images were used as input images and original clean images were used as output images for training. |
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## Training procedure |
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### Training hyperparameter |
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The following hyperparameters were used during training: |
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- optimizer: 'adam' |
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- loss: 'binary_crossentropy' |
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- epochs: 100 |
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- batch_size: 128 |
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- 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> |
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<summary>View Model Plot</summary> |
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</details> |
