CNN Convolution Explorer
2D Convolution
A filter (kernel) slides over the input image with a given stride $S$ and padding $P$. At each position it computes a dot product with the overlapping patch, writing one value into the feature map. Use the controls below to explore how each parameter changes the result.
Input size
7×7
with padding: 7×7
Filter size
3×3
receptive field
Output size
5×5
feature map
Position
—
row, col (0-indexed)
Input Feature Map
7×7
conv2d
Output Feature Map
5×5
Filter / Kernel (click cells to cycle values)
Presets
Current dot product computation
Press Play or Step to begin.
Tip: Start with Padding = 0, Stride = 1, Filter = 3.
Then increase padding to see how the "virtual border" expands the input —
with zero padding (black) the border is 0, with one padding (white) it is 1.
Increase the stride to watch the kernel jump further, producing a smaller feature map.