Issues with Model Training and Overfitting in PyTorch

vision
1

Hello everyone,

I’m reaching out for help regarding an issue I’m facing while training my model in PyTorch.

Issue: I’ve noticed that my model is overfitting significantly on the training data, which is affecting its performance on the validation set.

Details:

  • I’m using PyTorch version [insert version number].
  • The architecture I’m working with is [insert model architecture, e.g., CNN, RNN].
  • My training dataset consists of [insert dataset size and type].
  • I’ve implemented [mention any techniques you’ve tried, e.g., dropout, data augmentation], but the overfitting persists.

Questions:

  1. What strategies can I employ to reduce overfitting in my model?
  2. Are there specific hyperparameters I should focus on adjusting?
  3. How can I effectively evaluate my model’s performance to ensure it’s generalizing well?

I appreciate any insights or advice you can provide!

Thank you for your help!

2

What type of task (e.g., classification, object detection)? How much data? How are you augmenting? HOw do you split into training/validation? What are you doing? Do you have code you can share a link to?