Overfitting is a significant issue in computer vision where models learn training data too well, including noise and irrelevant details, leading to poor performance on new unseen data. This occurs when models memorize specific patterns in the training images instead of learning general features. Overfit models have extremely high accuracy on the training data but much lower accuracy on testing data, failing to generalize well. High model complexity relative to data size, noisy training data, insufficient regularization, and data leakage are some causes of overfitting in computer vision. To detect an overfit model, one can monitor training and validation/test error, plot learning curves, perform k-fold cross-validation, apply regularization techniques, analyze model complexity, and use visualization tools. Various methods such as data augmentation, early stopping, dropout, L1 and L2 regularization, transfer learning, ensemble methods, and active learning can help avoid overfitting in computer vision. Encord Active is a comprehensive platform that offers features to curate a dataset, evaluate model performance, and identify potential issues with overfitting.