You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We use a new network for performing feature extraction. Our new network is a hybrid approach between the network used in YOLOv2, Darknet-19, and that newfangled residual network stuff. Our network uses successive 3\times 3 and 1\times 1 convolutional layers but now has some shortcut connections as well and is significantly larger. It has 53 convolutional layers so we call it…. wait for it….. Darknet-53! When we plot accuracy vs speed on the AP{}_{50} metric (see figure 5) we see YOLOv3 has significant benefits over other detection systems. Namely, it’s faster and better. So here’s the deal with YOLOv3: We mostly took good ideas from other people. We also trained a new classifier network that’s better than the other ones. We’ll just take you through the whole system from scratch so you can understand it all.

Question: How does YOLOv3 improve upon previous versions of the YOLO object detection algorithm?
YOLOv3 is faster and better than YOLO. It has more layers. The authors also tried some small tricks and experiments which further improved the overall performance.