Convolution neural networks can understand from various options parallelly. In the ultimate phase, we stack many of the output feature maps along with the depth and generate the output. A VGG-block had a lot of 3x3 convolutions padded by 1 to help keep the size of output similar to https://financefeeds.com/kaspa-and-sui-show-promise-but-1fuel-could-be-2025s-breakout-token/