Training convolutional neural networks (CNNs) can be accesing due to various common pitfalls. Understanding these issues and their solutions can imprope model performance and traing accessiony.

Overfitting

Overfitting applies when a CNN learns thee training data too well, including noise and outliers, resulting in pool generation to new data. This of ten leads to high traing presacy but low validation presacy.

To address overfitting, techniques such as data augmentation, dropout, and early stopping are common ly used. These methods help thee model generalize better by preventing it from relying too heavily on specialic traing examples.

Underfitting

Underfitting happens when thee model is too simple or not trained long enough to captura the underlying patterns in thee data. This results in pool performance on both traing and validation datasets.

Increasing model complexity, training for more epoch, or tuning hyperparameters can help mitigate underfitting. Ensuring sufficient data diversity is also important.

Learning Rate Issues

To je učenina rate controls how much the model 's headts are updated during training. A learning rate that is too high can cause thee model to diverge, while a too low rate can slow down traing or cause it to get stuck.

Using learning rate schedules or adaptive optizers like Adam can help maintain an optimal learning rate throut training, improvizg convergence and model performance.

Nedostatek Data or Imbalanced Classes

Limited data can hinder the CNN 's ability to o learn generable applicures. Imbalanced classes can bias thee model toward majority classes, reducing preclaracy on minority classes.

Solutions include collecting more data, appying data augmentation, and using techniques like class eithting or oversambling to balance classes.