readme: ---
  license: gpl-3.0
  ---
  
  ## Model Description
  
  ### Keras Implementation of Convolutional autoencoder for image denoising
  
  This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.
  
  Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder
  
  Keras Example Link:- https://keras.io/examples/vision/autoencoder/
  
  ## 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.
  
  ## 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.
  
  ## 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.
  
  ## Model Plot
  
  <details>
  <summary>View Model Plot</summary>
  
  ![Model Image](./model.png)
  
  </details>