Receptive Field & Parameter Explorer
Stack convolution layers, add pooling and dilation, and watch how quickly — or slowly — a network gains a global view of the image.
- receptive field
- CNN architecture
- pooling
- dilation
Final receptive field
18×18 px
Final spatial resolution
32×32
Total conv parameters
18816.000
Final receptive field: 18×18 pixels
Receptive field growth vs. depth
Architecture summary
Input size: 128 × 128 × 1
Layers: 3
Kernel: 3 × 3, dilation 1
Pooling: Yes (2×2)
Filters per layer: 32
Final spatial resolution: 32 × 32
Final receptive field: 18 × 18
Total conv parameters: 18,816
⚠ Receptive field does not cover the full input — the final layer cannot see the whole image at once.
Pooling buys receptive field, dilation buys it for free
Pure 3×3 convolutions add only 2 pixels of receptive field per layer — reaching a 512-pixel image would take roughly 256 stacked layers. Pooling (or strided convolution) halves spatial resolution each time, which doubles the effective receptive field of every subsequent layer: exponential growth instead of linear. Dilation inserts zeros into the kernel, expanding its footprint without adding parameters — a 3×3 kernel with dilation rate 2 sees a 5×5 region but still has 9 weights. Parameter count, meanwhile, grows with the product of input and output filters, independent of receptive field.