# Understanding the output shape of the following YOLO network

Below you can see a convolutional network with 24 convolutional layers. I am trying to understand the shape of the network. Given the input image with shape 448x448x3, we apply first conv filter of shape 7x7x64 with stride = 2 followed by a maxpool layer with 2x2 with stride 2. The output as I calculated it is as follows:

$$o_{conv}=\frac{448-7}{2}+1=221 \\ o_{pool}=\frac{221-2}{2}+1=110.5$$

I am not sure why we got the shape to be 112, please.

• Which Yolo is this? i.e. YoloVx. Commented Dec 24, 2022 at 11:22
• @gunes. YOLOv1 model
– Avv
Commented Dec 24, 2022 at 13:15

I couldn't find YoloV1 TFlite model file and verify, but the option that can cause this is padding. There are two modes of padding: SAME and VALID. In the SAME option, the input image is padded from left/right and top/bottom such that the output will be of the same size (when strides are 1). For that, you need 6 pixels of padding. And, with stride equal to 2, you'll have $$o_{\text{conv}}=\bigg\lfloor{\frac{448-7+6}{2}}\bigg\rfloor+1=224$$
When we pool this with $$2\times 2$$ filters, we get $$112$$ pixels.