In the Name of God, the Beneficent, the Merciful
This is the implementation of image translation using Unet Model in pytorch.
You can see the code and result in Image _To_Image_Translation.ipynb .
The U-Net model is a popular architecture used for image translation tasks. It is particularly effective in tasks such as image segmentation, where the goal is to classify and differentiate different regions within an image. The U-Net architecture consists of an encoder-decoder structure with skip connections.
The encoder part of the network captures the high-level features of the input image through a series of convolutional and pooling layers.
The decoder part of the network uses upsampling and transposed convolutions to generate a high-resolution output that matches the input image size.
The skip connections, which connect corresponding layers from the encoder to the decoder, help preserve spatial information and aid in the accurate reconstruction of the output.
The UNet model has gained popularity due to its ability to handle both local and global features effectively. I used UNet in this implementation.
You can see the results of this implementation in below picture:
Input images of test dataset are in the first row.
Predicted images (outputs of model) are in the second row.
