This article is a continuation of the work done in the previous article. It will summarize other methods used by the best COCO ranking architectures such as YOLOR, CBNet and R-CNN series, CenterNet, DetectoRS and EfficientDet.

The aim of this article is to give a state of the art of object detection evaluated on COCO and classified by architecture type. Then, the transformers will be explained starting from the NLP domain to their adaptation to the computer vision domain with the Swin Transformers and the Focal Transformers. The methods presented in the SwinV2-G paper to adapt the Swin Transformer to a 3 billion parameters model will also be explained.