transformer-based detector SWINL Cascade-Mask R-CNN

The SWINL Cascade-Mask R-CNN is a state-of-the-art object detection model that is based on the transformer architecture. It is a variant of the popular Mask R-CNN model, which uses a two-stage approach to detect objects in an image.

The SWINL Cascade-Mask R-CNN model uses a hierarchical feature pyramid network (FPN) to extract multi-scale features from an input image. These features are then processed by a series of transformer-based layers to further refine the representation of the image.

One of the key innovations of the SWINL Cascade-Mask R-CNN model is the use of a sliding window approach to process the image. This allows the model to efficiently process large images without requiring excessive memory or computational resources.

The model also uses a cascaded architecture, where the output of one stage is used as the input to the next stage. This helps to improve the accuracy of the model by refining the output at each stage.

Overall, the SWINL Cascade-Mask R-CNN model is a highly accurate and efficient object detection model that is well-suited for a wide range of applications, including image recognition, video analysis, and autonomous driving.