juniorComputer Vision
What is overfitting in deep learning models?
Updated May 15, 2026
Short answer
Overfitting occurs when a model learns training data too well but fails on unseen data.
Deep explanation
It happens when a model memorizes noise instead of learning general patterns. Regularization, dropout, and more data help prevent it.
Real-world example
A model that works well in training but fails in real-world surveillance.
Common mistakes
- Using overly complex models for small datasets.
Follow-up questions
- What is dropout?
- How to detect overfitting?