Conv2D Output Size Calculator

Use this calculator to compute the output height and width of a Conv2D (2D convolutional) layer in a convolutional neural network, using standard deep learning formulas.

Input width:

Input height:

Kernel width:

Kernel height:

Padding width:

Padding height:

Stride width:

Stride height:

Dilation width:

Dialtion height:

What this calculator is doing

This calculator uses the standard Conv2D output size formula: \[ \text{Output Dimension} = \left\lfloor \frac{\text{Input Dimension} + 2 \times \text{Padding} - \text{Dilation} \times (\text{Kernel Size} - 1) - 1}{\text{Stride}} \right\rfloor + 1 \] It applies the formula separately for height and width: \[ \text{Output Height} = \left\lfloor \frac{H_{\text{in}} + 2P_h - D_h \times (K_h - 1) - 1}{S_h} \right\rfloor + 1 \] \[ \text{Output Width} = \left\lfloor \frac{W_{\text{in}} + 2P_w - D_w \times (K_w - 1) - 1}{S_w} \right\rfloor + 1 \] Where:
- \( H_{\text{in}} \), \( W_{\text{in}} \): Input dimensions
- \( K_h \), \( K_w \): Kernel dimensions
- \( P_h \), \( P_w \): Padding
- \( S_h \), \( S_w \): Stride
- \( D_h \), \( D_w \): Dilation (optional, default = 1)

This is critical when building CNN architectures to ensure the correct output shape flows through your model layers.

Looking for similar calculators?

Check out the following calculators as well! They provide similar solutions to this one.

Want to reuse this calculator?

You can embed this calculator to your own website using the following endpoint: https://www.nichecalcs.com/embed/conv2d_outputsize

Find out more about our Services

Disclaimer: These calculators are provided for informational purposes only. Always verify your designs against relevant engineering standards and consult a qualified professional. We do not take responsibility for any errors or damages resulting from the use of these calculations.