Kernel Calculator

The Kernel Size Calculator helps users determine the output dimensions, receptive field size, and padding requirements for a given convolutional kernel and stride in a neural network.

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Step-by-Step Guide to Using the Kernel Size Calculator

Introduction

This guide will walk you through the steps of using the Kernel Size Calculator to determine various parameters related to convolution operations in the context of image processing.

Step 1: Input Kernel Dimensions

  1. Kernel Width: Enter the desired kernel width in the input field labeled “Kernel Width.” Ensure that this is an odd number between 1 and 15.
  2. Kernel Height: Enter the desired kernel height in the field labeled “Kernel Height.” Like the width, this should be an odd number between 1 and 15.

Step 2: Define the Stride

Input the stride value in the “Stride” field. This should be a number ranging from 1 to 5. The stride determines how the filter convolves around the input image.

Step 3: Input Image Dimensions

  1. Input Width: Fill in the width of the input image under “Input Width.” This should be a positive number.
  2. Input Height: Enter the height of the input image into the “Input Height” field, ensuring it is a positive number.

Step 4: Choose Padding Type

Select the type of padding from the “Padding Type” dropdown. You have two options:

  • Valid: No padding will be added to the image.
  • Same: Maintains the dimensionality of the input image, padding borders as necessary.

Step 5: Review Calculated Results

After entering all the necessary inputs, the calculator will automatically compute and display the following results:

  1. Output Width: The calculated width of the output image, expressed in pixels.
  2. Output Height: The computed height of the output image, in pixels.
  3. Receptive Field Size: The size of the receptive field in pixels, representing the area of the input image that contributes to one output pixel.
  4. Padding Width (each side): For ‘Same’ padding, this shows the padding width applied on each side of the input.
  5. Padding Height (each side): The computed padding height applied on each side of the input image when using ‘Same’ padding.

Conclusion

By following these steps, you can efficiently utilize the Kernel Size Calculator to determine critical parameters needed for convolution operations in image processing applications.