How to detect an Overfitting Computer Vision model?

Overfitting can be detected in computer vision models using the following techniques:

1. Monitor Performance Metrics: Record the number of elements accurately classified or the number of misclassified elements on both the training and validation data. The sign and magnitude change in a certain round are overfitting.
2. Use Cross-Validation: Improve the approach further by partitioning the dataset into a combination of folds and then run the model to verify its performance across those folds.
3. Visualize Learning Curves: Prepare and plot the training and validation curves to show loss/accuracy estimated by model over the epoch. When remote points of the curves are far from each other, this is a sign of overfitting.
4. Evaluate with a Hold-Out Test Set: To evaluate the generality of the sources, validate the model on a test dataset that was not involved in the training.

Overfitting is a common problem in machine learning, especially in computer vision tasks where models can easily memorize training data instead of learning to generalize from it. Handling overfitting is crucial to ensure that the model performs well on unseen data.

In this article, we are going to explore the techniques and methods to handle overfitting in computer vision models.