VFNet is short for VariFocalNet. This method of object detection was first released in 2008 and it scored 55.1 on the COCO test-dev benchmark, state-of-the-art at the time. There have since been other improvements.
The original paper is here. The implementation of this model is here.
The problem it solves is that when we’re training a model, we have a large number of possible options for objects detected in an image. What we need to do is rank these options in order of likelihood of being a correct bounding of a box.
It is based on and draws on the MMDetection model/toolbox. MMDetection is a Pytorch library for object detection. It is modular, allowing for greater customisability.