I am trying to apply a self supervised task as stated in this github repo.The Self-Supervised Sketch Recognition
In this work, authors are using 345.000 image samples to train the model and the dataset is constructed by rotating the images in 0/90/180/270 degrees. So the number of classes is 4.
When I train the model, I can get the best model recorded as the best epoch with the above parameters. (Alexnet is used in training with *.png files)
Then I need to apply this model and weights to downstream tasks, where there are 345 different classes. (But the trained model is with 4 classes) I know they do not need to be the same but I am confused how to make transfer learning..
Should I train the model again with the weights I received in pretext tasks? Or what should I do? Any reference or sample project is greatly welcome.
Thanks in advance...