I'm implementing YOLO network and have some questions. In the [original paper](https://arxiv.org/pdf/1506.02640.pdf) the authors say: "For pretraining we use
the first 20 convolutional layers from Figure 3 followed by a
average-pooling layer and a fully connected layer". And also they report that they use ImageNet 1000 classes dataset and 224x224 input size instead of 448x448
![Figure 3](https://jamiekang.github.io/media/2017-06-18-you-only-look-once-unified-real-time-object-detection-fig3.jpg)

My questions are the following:

1) What is the size of average-pooling layer kernel? 2x2?

2) How do authors reduce the input size to 224x224? Do they omit the 1st layer?