Update README.md

README.md

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 license: gpl-3.0
 ---
 
-# Keras Implementation of Convolutional autoencoder for image denoising
+## 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.
 
@@ -10,6 +12,26 @@ 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>