Increasing the depth of a neural network always improves its performance in image classification tasks, regardless of the dataset characteristics.

Increasing the depth of a neural network does not guarantee better image classification performance because deeper models can overfit, especially when the dataset is small or noisy, and they may suffer from vanishing gradients or training instability. A deeper network can learn more complex features, but if the data does not contain enough variation or if the network is too large, the extra layers simply memorize training examples instead of learning general patterns. For example, a 50‑layer network trained on a tiny dataset of handwritten digits may perform worse than a shallow 5‑layer network because the extra layers overfit to the few training images. Thus, depth must be matched to the dataset size, quality, and the problem’s complexity, and regularization or architectural tricks are often needed to reap the benefits of deeper models.