Object Detection Models
VarifocalNet
Introduced by Zhang et al. in VarifocalNet: An IoU-aware Dense Object DetectorVarifocalNet is a method aimed at accurately ranking a huge number of candidate detections in object detection. It consists of a new loss function, named Varifocal Loss, for training a dense object detector to predict the IACS, and a new efficient star-shaped bounding box feature representation for estimating the IACS and refining coarse bounding boxes. Combining these two new components and a bounding box refinement branch, results in a dense object detector on the FCOS architecture, what the authors call VarifocalNet or VFNet for short.
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% |
Varifocal Loss
