**YOLOv2**, or [**YOLO9000**](https://www.youtube.com/watch?v=QsDDXSmGJZA), is a single-stage real-time object detection model. It improves upon [YOLOv1](https://paperswithcode.com/method/yolov1) in several ways, including the use of [Darknet-19](https://paperswithcode.com/method/darknet-19) as a backbone, [batch normalization](https://paperswithcode.com/method/batch-normalization), use of a high-resolution classifier, and the use of anchor boxes to predict bounding boxes, and more.

This is **Graph Transformer** method, proposed as a generalization of [Transformer](https://paperswithcode.com/method/transformer) Neural Network architectures, for arbitrary graphs.

Compared to the original Transformer, the highlights of the presented architecture are:

- The attention mechanism is a function of neighborhood connectivity for each node in the graph.  
- The position encoding is represented by Laplacian eigenvectors, which naturally generalize the sinusoidal positional encodings often used in NLP.  
- The [layer normalization](https://paperswithcode.com/method/layer-normalization) is replaced by a [batch normalization](https://paperswithcode.com/method/batch-normalization) layer.  
- The architecture is extended to have edge representation, which can be critical to tasks with rich information on the edges, or pairwise interactions (such as bond types in molecules, or relationship type in KGs. etc).

Graph Transformer

A Generalization of Transformer Networks to Graphs

YOLOv2

YOLO9000: Better, Faster, Stronger

**PVT**, or **Pyramid Vision Transformer**, is a type of [vision transformer](https://paperswithcode.com/methods/category/vision-transformer) that utilizes a pyramid structure to make it an effective backbone for dense prediction tasks. Specifically it allows for more fine-grained inputs (4 x 4 pixels per patch) to be used, while simultaneously shrinking the sequence length of the Transformer as it deepens - reducing the computational cost. Additionally, a [spatial-reduction attention](https://paperswithcode.com/method/spatial-reduction-attention) (SRA) layer is used to further reduce the resource consumption when learning high-resolution features.

The entire model is divided into four stages, each of which is comprised of a patch embedding layer and a $\mathcal{L}\_{i}$-layer Transformer encoder. Following a pyramid structure, the output resolution of the four stages progressively shrinks from high (4-stride) to low (32-stride).

Source	YOLO9000: Better, Faster, Stronger
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com