Is the Yolo V2 loss function the same as the loss function?

Is the Yolo V2 loss function the same as the loss function?

The two losses are different and the lack of explicit formula in the Yolo v2 loss paper rises some confusion, for example at What is YOLOv2 Loss Function – Google Groups. The YOLO v1 is difined in You Only Look Once:Unified, Real-Time Object Detection as:

When to use member 1ij OBJ for Yolo loss?

All of them are Mean-Squared error losses and are modulated by some scalar meta-parameter or IoU score between the prediction and ground truth: The member 1ij obj member is used to modulate the loss based on the presence of an object on a particular cell i, j:

Why does Yolo loss function depend on anchor box index?

When we move to anchor boxes we also decouple the class prediction mechanism from the spatial location and instead predict class and objectness for every anchorbox. This means that the confidence probability pi(c) above should depend not only on i and c but also an anchor box index, say j. Therefore, the loss needs to be different from above.

How to use Yolo V1 for object detection?

The YOLO v1 is difined in You Only Look Once:Unified, Real-Time Object Detection as: λcoord ∑S2 i = 0 ∑B j = 0Lobj ij [(xi − ˆxi)2 + (yi − ˆyi)2] + λcoord ∑S2 i = 0 ∑B j = 0Lobj ij [(√wi − √ˆwi)2 + (√hi − √ˆhi)2] + ∑S2 i = 0 ∑B j = 0Lobj ij (Ci − ˆCi)2 + λnoobj ∑S2 i = 0 ∑B j = 0Lnoobj ij (Ci − ˆCi)2 + ∑S2 i = 0Lobj i ∑c ∈ classes(pi(c) − ˆpi(c))2

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).

Which is the highest penalty in Yolo loss function?

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). The last part of the loss function is the classification loss: