Yolo explanation

1. How do you explain Yolo?
2. What is good about Yolo?
3. How do you read Yolo?
4. How to use Yolo for object detection?

How do you explain Yolo?

What is YOLO? YOLO is an abbreviation for the term 'You Only Look Once'. This is an algorithm that detects and recognizes various objects in a picture (in real-time). Object detection in YOLO is done as a regression problem and provides the class probabilities of the detected images.

What is good about Yolo?

YOLO is extremely fast because it does not deal with complex pipelines. It can process images at 45 Frames Per Second (FPS). In addition, YOLO reaches more than twice the mean Average Precision (mAP) compared to other real-time systems, which makes it a great candidate for real-time processing.

How do you read Yolo?

YOLO is based on the idea of segmenting an image into smaller images. The image is split into a square grid of dimensions S×S, like so: Credit: research paper. The cell in which the center of an object, for instance, the center of the dog, resides, is the cell responsible for detecting that object.

How to use Yolo for object detection?

YOLO (You Only Look Once) is a method / way to do object detection. It is the algorithm /strategy behind how the code is going to detect objects in the image. The official implementation of this idea is available through DarkNet (neural net implementation from the ground up in C from the author).