Answers.Body: <p>For those who happened to come across my question, I solved my problem.</p>
  <p>After training the model, I initiated the network with the trained parameters (weights). Then as I have a loaded model, I removed the last output neurons, which was 4 ea. and I added 345 neurons.</p>
  <p>Afterwards, I froze the all neurons's weights except the last last layer of 345 and trained the model again to adjust last added weights.</p>
  <p>Then voila!</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-04-12T05:31:54.690Z
Answers.LastActivityDate: 2023-04-12T05:31:54.690Z
Answers.UserDisplayName: T.Gulez
Body: <p>I am trying to apply a self supervised task as stated in this github repo.<a href="https://github.com/zzz1515151/self-supervised_learning_sketch" rel="nofollow noreferrer">The Self-Supervised Sketch Recognition</a></p>
  <p>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.</p>
  <p>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)</p>
  <p>Then I need to apply this model and weights to <strong>downstream tasks</strong>, 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..</p>
  <p>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.</p>
  <p>Thanks in advance...</p>
  
Tags: <transfer-learning><self-supervised-learning><pretext-tasks>
Title: Self Supervised Learning Application of trained model... A bit confused
UserDisplayName: T.Gulez
ThreadText: # Self Supervised Learning Application of trained model... A bit confused
  
  (Asked by: username_0 on 2023-04-10)
  
  I am trying to apply a self supervised task as stated in this github repo.[The Self-Supervised Sketch Recognition](https://github.com/zzz1515151/self-supervised_learning_sketch)
  
  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...
  
  **Tags:** transfer-learning, self-supervised-learning, pretext-tasks
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2023-04-12. Score: 0 | Accepted answer
  
  For those who happened to come across my question, I solved my problem.
  
  After training the model, I initiated the network with the trained parameters (weights). Then as I have a loaded model, I removed the last output neurons, which was 4 ea. and I added 345 neurons.
  
  Afterwards, I froze the all neurons's weights except the last last layer of 345 and trained the model again to adjust last added weights.
  
  Then voila!
  
  
  ---
  
  