CNN Receptive Field Calculator

CNN Receptive Field Calculator

How much of the original input image actually influences a single output neuron deep in a CNN? This calculator tracks receptive field growth across stacked layers.

Formula

RF_new = RF_old + (Kernel Size - 1) x Cumulative Stride So Far

Example

Three layers with kernel sizes 3,3,3 and strides 1,2,2:

Final Receptive Field: 9 x 9 pixels

Why Downsampling Layers Matter So Much

Receptive field grows especially fast when earlier layers use strided or pooled downsampling, since that downsampling compounds the effective coverage of every subsequent layer's kernel - a layer with stride 2 doesn't just double its own receptive field contribution, it doubles the multiplier applied to every layer that comes after it too.

Why This Matters for Model Design

Understanding receptive field size is particularly important for tasks like object detection and semantic segmentation, where the receptive field needs to be large enough to "see" the full extent of the objects being detected - a receptive field smaller than the target object means the network physically cannot gather enough context to recognize it correctly, regardless of how well-trained the model is.