May 2020
Beginner to intermediate
430 pages
10h 39m
English
Content preview from Mastering Computer Vision with TensorFlow 2.x
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Stride
Normally, in a convolution, we move the kernel by one step, apply a convolution to that step, and so on. The stride allows us to skip a step. Let's take a look:
- When stride = 1, we apply normal convolution without skipping.
- When stride = 2, we skip a step. This reduces the image size from 7 x 7 to 3 x 3 (see the following diagram):
Here, each 3 x 3 window shows the results of skipping one step. The result of a stride is to shrink the dimension since we are skipping over potential x, y locations.
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