Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
GNU General Public License v3.0
8.95k
stars
1.42k
forks
source link
Hello, why is the pooling layer placed before the convolution layer in downsampling, while I've learned that it's usually placed after? What advantages does this approach offer? #580
1.Reducing the spatial dimensions early on decreases the computational load in subsequent layers.
2.Pooling before convolution emphasizes the most significant features.
3.Noise suppression.
There could be several reasons for this:
1.Reducing the spatial dimensions early on decreases the computational load in subsequent layers. 2.Pooling before convolution emphasizes the most significant features. 3.Noise suppression.