Loss Functions
Varifocal Loss
Introduced by Zhang et al. in VarifocalNet: An IoU-aware Dense Object DetectorVarifocal Loss is a loss function for training a dense object detector to predict the IACS, inspired by focal loss. Unlike the focal loss that deals with positives and negatives equally, Varifocal Loss treats them asymmetrically.
$$ VFL\left(p, q\right) = −q\left(q\log\left(p\right) + \left(1 − q\right)\log\left(1 − p\right)\right) \text{ if } q > 0 $$
$$ VFL\left(p, q\right) = −\alpha{p^{\gamma}}\log\left(1-p\right) $$
where $p$ is the predicted IACS and $q$ is the target IoU score.
For a positive training example, $q$ is set as the IoU between the generated bounding box and the ground-truth one (gt IoU), whereas for a negative training example, the training target $q$ for all classes is $0$.
Source: VarifocalNet: An IoU-aware Dense Object Detector
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 4 | 50.00% |
| Small Object Detection | 1 | 12.50% |
| Instance Segmentation | 1 | 12.50% |
| Semantic Segmentation | 1 | 12.50% |
| General Classification | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
