Early Stopping Explained!

Early stopping is one of the effective and simplest regularization techniques used in training neural networks. Usually, during training, the training loss will decrease gradually, and if everything goes well on the validation side, validation loss will decrease too. When the validation loss hits the local minimum point, it will start to increase again. Which is a signal of overfitting. How can we stop the training just right before the validation loss rise again? Or before the validation accuracy starts decreasing?