Deep neural networks are powerful but can overfit data. A new method combines models from different training stages to prevent overfitting without losing performance. By using a consensus of predictions from these models, the new method outperforms traditional ensembles and early stopping techniques. It is easy to implement and works well with various datasets and network architectures.