Introduction:
For this week’s assignment, I trained the summer2winter yosemite dataset with CycleGAN on Intel DevCloud. Domain A contains images of Yosemite Park in the summer and Domain B in the winter. There are no big differences in style between the two domains as most of the landscapes remain the same except for the addition of snow in the winter.
Process:
Due to some error when saving the training script, I wasted some time initially retraining the monet2photo dataset again. Therefore there was not as much time for the summer2winter dataset training which contains around 1000 images. Right now I am at 58 epochs after 24 hours. It will be trained up to 200 epochs which should take a little less than three more days.
Results:
Here are the results achieved after inferencing with 58 epochs
Conclusion:
The results are actually worse than I expected. If we look closely, we can see the skies in the inferenced images get gloomier and trees appear to be in a darker shade of green. However, there is no trace of snow to signify winter which is rather disappointing.
In general, there are no big differences between the left and right images which suppose to correspond to summer and winter respectively. I suspect this is due to the small number of epochs trained. Another possible reason might be images from Domain A and B are quite similar in style.
The results right now are not great. I will continue to train and inference with a better model once it is completed.