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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
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+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+*This model was published in HF papers on 2021-02-24 and contributed to Hugging Face Transformers on 2023-07-24.*
+
+# Pyramid Vision Transformer (PVT)
+
+
+## Overview
+
+The PVT model was proposed in
+[Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://huggingface.co/papers/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
+
+## PvtImageProcessorPil
+
+[[autodoc]] PvtImageProcessorPil
+    - preprocess
+
+## PvtForImageClassification
+
+[[autodoc]] PvtForImageClassification
+    - forward
+
+## PvtModel
+
+[[autodoc]] PvtModel
+    - forward