YOLOv7 (2022)

In this lesson, we will discuss the novelties of YOLOv7 in detail:

• E-ELAN
• Compound scaling for concatenation blocks
• Planned reparameterized convolution

To understand these structures better, we need to first examine some more basic components.

Group convolution

We learned network-in-network architecture, in which the input feature map passes through different convolutions in one layer and is concatenated to build the single output feature map.

Suppose that we have different convolutions in a layer, but instead of applying them to the whole input feature map, we divide the input into groups, and each group goes to one specific convolution. Concatenating the output from each convolution, we obtain the output feature map. ​​This process is called group convolution.