Conv2D Output Size Calculator
Output H×W of a 2-D convolution from input size, kernel, stride, padding and dilation — with the floor-division gotcha.
Defaults are ResNet's stem: 224×224 input, 7×7 kernel, stride 2, padding 3 → 112×112. The floor division silently drops pixels when (in+2p−k) isn't divisible by the stride — the classic off-by-one debugging trap.
Formula
About Conv2D Output Size Calculator
Every CNN debugging session eventually arrives at the same question: what size does this conv layer output? This calculator implements the exact PyTorch/TensorFlow formula — including dilation and the floor division that silently swallows edge pixels when shapes don't divide evenly. Defaults show ResNet's famous 7×7-stride-2 stem turning 224×224 into 112×112. Chain it mentally (or with our receptive-field tool) through your network to find where a shape mismatch is born instead of reading framework stack traces backwards.
How to use Conv2D Output Size Calculator
- 1Enter your values into Conv2D Output Size Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Conv2D Output Size Calculator?
- ✓Computes Conv2D Output Size instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: out = ⌊(in + 2.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why did my output shrink by one pixel from what I expected?+
Floor division. If (input + 2·padding − kernel) is not an exact multiple of stride, the formula floors, discarding the leftover rows/columns at the bottom-right. TensorFlow's 'SAME' mode pads asymmetrically to avoid this; PyTorch makes you pick padding yourself.
What padding keeps the size unchanged ('same' convolution)?+
For stride 1 and odd kernel k: padding = (k−1)/2 — so 1 for 3×3, 2 for 5×5, 3 for 7×7. With dilation d: padding = d·(k−1)/2. For even kernels true 'same' needs asymmetric padding, which is why even kernels are rare.
How does dilation change the output size?+
Dilation d spreads the kernel's taps apart, making its effective size d·(k−1)+1: a 3×3 at dilation 2 covers a 5×5 footprint. Output shrinks accordingly unless padding compensates — the formula in this tool handles it exactly.
Does the channel count affect output H×W?+
No — channels are orthogonal to spatial geometry. Output channels equal the number of filters you choose; only kernel, stride, padding and dilation determine the spatial size. (Channels do drive parameters and FLOPs — see our Conv2D parameter and FLOPs tools.)
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