class Dropout2d(p: float = 0.5)#

Apply 2D channel-wise dropout during training.

Randomly zero out entire channels independently with probability \(p\). This layer expects the channels to be last, i.e. the input shape should be NWHC or WHC where:N is the batch dimension,``H`` is the input image height,``W`` is the input image width, and``C`` is the number of input channels

The remaining channels are scaled by \(\frac{1}{1-p}\) to maintain the expected value of each element. Unlike traditional dropout, which zeros individual entries, this layer zeros entire channels. This is beneficial for early convolution layers where adjacent pixels are correlated. In such case, traditional dropout may not effectively regularize activations. For more details, see [1].

[1]: Thompson, J., Goroshin, R., Jain, A., LeCun, Y. and Bregler C., 2015. Efficient Object Localization Using Convolutional Networks. CVPR 2015.


p (float) – Probability of zeroing a channel during training.