YOLO? You Only Live Once! Go and take adventures before we waste life in the common days, as in The Motto by Drake.
Well, maybe we should go back from the lecture hall of PCS100 (Popular Culture Study) to the classroom of computer science and statistics. In the world of algorithms, YOLO refers to You Only Look Once. Its name has indicated that it is very powerful with full confidence on its efficiency. But what is such a powerful algorithm and how does it work?
YOLO is an algorithm of bounding box regression that performs object detection. It can recognize the classes of objects in images and bound those objects with predicted boxes, where the tasks of classification and localization are completed at the same time. Compared with previous region-based algorithms like R-CNN, YOLO is more efficient because it is region-free.
Object detection methods usually use sliding windows to go through the whole image and see whether there is an object in each window. Region-based algorithms like R-CNN apply Region Proposal to reduce the number of windows to check. YOLO is different as it makes predictions on the entire image at the same time. As an analogy for fishing, R-CNN first divides the regions and picks those regions where fish might occur, while YOLO puts a fishing net and catch fishes together. YOLO divides the image into grids where each grid recognizes an object whose center is inside the grid by its bounding boxes. When several grids declare that an object occurs inside, non-maximal suppression is applied to only keep the grid with highest confidence. Thus, the combination of grid confidence and grid predicted bounding boxes could tell the final classification and localization of each object in the image.
As the development of region-free algorithms, there have been several versions of YOLO. One practical and advanced version is YOLOv3, which is also the version that I put in my project. It is widely applied in many fields, including the popular auto-driving and … also medical imaging analysis! YOLOv3 is popular because of its efficiency and simple usage, which could save much time for any potential user.
Now we can go to the fun part! Using YOLO in a sentence by the end of the day (I put both serious and not together):
Manager: “Where is Kolbe? He was supposed to finish his task of detecting all the tumors in these CT images tonight! Had he already gone through all thousands of images during the past hour?”
Yvonne: “Well, he was pretty stressed about his workload and asked me if there is any quick method that can help. I said YOLO.”
Manager: “That sounds good. The current version has good performance in many fields, and I bet it could help. Wait, but where did he go? He should be training models right now.”
Yvonne: “No idea. He just got excited and shouted YOLO, turned off the computer and left quickly without any message. I guess he was humming like Tik Tok when phoning with his friends.”
Manager: “Okay, I can probably guess what happened. I need a talk with him tomorrow…”
See you in the blogosphere!