What are the different components of the loss function in Yolo?

What are the different components of the loss function in Yolo?

As the localizer, the YOLO loss function is broken into three parts: the one responsible for finding the bounding-box coordinates, the bounding-box score prediction, and the class-score prediction.

What is the loss function used in Yolo object detection?

NOTE: In the original YOLO paper, the loss function uses Ci instead of Cij as confidence score. I made the correction based on my own understanding, since every bounding box should have its own confidence score.

How does Yolo model work?

YOLO algorithm employs convolutional neural networks (CNN) to detect objects in real-time. As the name suggests, the algorithm requires only a single forward propagation through a neural network to detect objects. This means that prediction in the entire image is done in a single algorithm run.

How does Yolo calculate loss?

YOLO uses sum-squared error between the predictions and the ground truth to calculate loss….The loss function composes of:

1. the classification loss.
2. the localization loss (errors between the predicted boundary box and the ground truth).
3. the confidence loss (the objectness of the box).

What is the output of Yolo V3 loss function?

For starters, in YOLOv3 the output is S × S × B × ( 4 + 1 + C) as opposed to S × S × ( B × ( 4 + 1) + C), meaning that the last term would be 1 i j o b j. See here for a full explanation of these equations, but they simply describe the coordinates of x and y, width and height, and finally the probability of an object being present in the cell.

When do you use the λ parameter in Yolo?

The λ parameters that appear here and also in the first part are used to differently weight parts of the loss functions. This is necessary to increase model stability. The highest penalty is for coordinate predictions ( λ coord = 5) and the lowest for confidence predictions when no object is present ( λ noobj = 0.5).

Is the Yolo loss function a mean squared error?

All losses are mean-squared errors, except classification loss, which uses cross-entropy function. Now, let’s break the code in the image. We need to do this for each of the 13×13 cells where S = 12 (since we start index from 0) ∑S2 i = 0 represents this part. is 1 when there is an object in the cell i, else 0.

What does 1objij mean in Yolo loss function?

It is the intersection over union of the predicted bounding box with the one from the label. 1objij “denotes that the j th bounding box predictor in cell i is responsible for that prediction”.