# Pyramid Vision Transformer (PVT)

## Overview

The PVT model was proposed in

[Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122)

by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. The PVT is a type of

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 (SRA) layer

is used to further reduce the resource consumption when learning high-resolution features.

The abstract from the paper is the following:

*Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a 

simpler, convolution-free backbone network useful for many dense prediction tasks. Unlike the recently proposed Vision 

Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer 

(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several 

merits compared to current state of the arts. Different from ViT that typically yields low resolution outputs and 

incurs high computational and memory costs, PVT not only can be trained on dense partitions of an image to achieve high 

output resolution, which is important for dense prediction, but also uses a progressive shrinking pyramid to reduce the 

computations of large feature maps. PVT inherits the advantages of both CNN and Transformer, making it a unified 

backbone for various vision tasks without convolutions, where it can be used as a direct replacement for CNN backbones. 

We validate PVT through extensive experiments, showing that it boosts the performance of many downstream tasks, including

object detection, instance and semantic segmentation. For example, with a comparable number of parameters, PVT+RetinaNet 

achieves 40.4 AP on the COCO dataset, surpassing ResNet50+RetinNet (36.3 AP) by 4.1 absolute AP (see Figure 2). We hope 

that PVT could serve as an alternative and useful backbone for pixel-level predictions and facilitate future research.*

This model was contributed by [Xrenya](<https://huggingface.co/Xrenya). The original code can be found [here](https://github.com/whai362/PVT).

- PVTv1 on ImageNet-1K

| **Model variant**  |**Size** |**Acc@1**|**Params (M)**|

|--------------------|:-------:|:-------:|:------------:|

| PVT-Tiny           |    224  |   75.1  |     13.2     |

| PVT-Small          |    224  |   79.8  |     24.5     |

| PVT-Medium         |    224  |   81.2  |     44.2     |

| PVT-Large          |    224  |   81.7  |     61.4     |

## PvtConfig

[[autodoc]] PvtConfig

## PvtImageProcessor

[[autodoc]] PvtImageProcessor

    - preprocess

## PvtForImageClassification

[[autodoc]] PvtForImageClassification

    - forward

## PvtModel

[[autodoc]] PvtModel

    - forward