Stride 

Stride basically refers to the step size we take when we slide the kernel across the 
input image.

An example of a stride of 1 is shown in the following screenshot:

Fig 6.10: Stride of 1

In a stride of 1, the value of the feature map is 9. Similarly, a stride of 2 looks as follows: 

Fig 6.11: Stride of 2

When we have a stride of 2, the value of the feature map will be 4, which is equivalent to 2 x 2.

Stride is important because of the following points:

• The stride controls the size of the convolution layer output.
• Using larger strides produces less overlap in kernels.
• Stride is one of the methods to control the spatial input size—that is, passing information to other input layers without losing it.