Loss types

[MattAlanWright]

I see in the training docs that there are tunable gains for three different loss types:

• Box loss
• Class loss
• DFL loss
  Can anyone tell me more about how these are calculated? Which ones might be tuned to emphasize recall over precision or precision over recall?

[glenn-jocher]

@MattAlanWright hello! The three loss types you mentioned are components of the overall loss function used to train YOLOv8 models:

• Box loss is responsible for the accuracy of the bounding box predictions. It measures how well the predicted boxes match the ground truth boxes in terms of location and size.

• Class loss evaluates the accuracy of the predicted object classes. It measures how well the model classifies the objects within the bounding boxes.

• DFL loss (Distribution Focal Loss) is a variant of the focal loss that focuses on hard-to-classify examples and is used to improve the model's performance on challenging cases.

To emphasize recall over precision, you might consider adjusting the gains associated with the box and class losses to be more forgiving of false negatives. Conversely, to emphasize precision over recall, you would adjust the gains to penalize false positives more heavily.

Keep in mind that tuning these gains should be done carefully, as it can affect the balance between precision and recall, and might require some experimentation to achieve the desired outcome. 🛠️