What are some ways to enhance PyTorch model with four classes?

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There are several ways to enhance a PyTorch model with four classes:

1. Increase the size of the model: increasing the number of layers in the model can improve its performance on complex datasets.

2. Use transfer learning: leveraging a pre-trained model and fine-tuning it for the specific task can help improve the model's accuracy.

3. Add regularization methods: techniques like dropout, weight decay, and early stopping can help prevent overfitting and improve the model's generalization performance.

4. Explore different loss functions: depending on the nature of the classification problem, using different loss functions like cross-entropy or binary cross-entropy can help improve the model's accuracy.

5. Augment the dataset: increasing the size of the dataset by generating new samples through data augmentation techniques like flipping, rotating or cropping the images can improve model performance.

6. Use ensemble methods: combining multiple models and their predictions can improve accuracy and reduce overfitting.