Increase the classifier head depth
Add Batch Normalization between dense layers
Use Dropout in the classifier head
Increase the number of units in dense layers
Use Global Average Pooling instead of flattening
Try a smaller learning rate
Use a better optimizer such as AdamW
Apply learning rate scheduling
Fine-tune more layers of the CNN backbone
Use a pretrained backbone
Improve input image resolution
Normalize and standardize inputs properly
Add data augmentation
Handle class imbalance with class weights or resampling
Use label smoothing
Increase training epochs with early stopping
Reduce overfitting with stronger regularization
Tune batch size
Use cross-validation for model selection
Clean mislabeled or noisy training data
Collect more training data
Try different activation functions
Experiment with different loss functions
Use mixed precision if it allows larger batch sizes
Freeze fewer layers during fine-tuning
Replace Flatten with pooling layers
Add residual connections in the head
Perform hyperparameter tuning
Monitor validation metrics, not just training accuracy