IDInit is a novel initialization method for neural networks that maintains identity transitions within layers, enhancing convergence, stability, and performance during training. By employing a padded identity-like matrix and addressing issues like dead neurons, IDInit offers a straightforward yet effective approach applicable to various deep learning models and large-scale datasets.