# DPT

## Overview

The DPT model was proposed in [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.

DPT is a model that leverages the [Vision Transformer (ViT)](vit) as backbone for dense prediction tasks like semantic segmentation and depth estimation.

The abstract from the paper is the following:

*We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense vision transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art.*

 DPT architecture. Taken from the original paper. 

This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/isl-org/DPT).

## Usage tips

DPT is compatible with the [`AutoBackbone`] class. This allows to use the DPT framework with various computer vision backbones available in the library, such as [`VitDetBackbone`] or [`Dinov2Backbone`]. One can create it as follows:

thon

from transformers import Dinov2Config, DPTConfig, DPTForDepthEstimation

# initialize with a Transformer-based backbone such as DINOv2

# in that case, we also specify `reshape_hidden_states=False` to get feature maps of shape (batch_size, num_channels, height, width)

backbone_config = Dinov2Config.from_pretrained("facebook/dinov2-base", out_features=["stage1", "stage2", "stage3", "stage4"], reshape_hidden_states=False)

config = DPTConfig(backbone_config=backbone_config)

model = DPTForDepthEstimation(config=config)

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DPT.

- Demo notebooks for [`DPTForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DPT).

- [Semantic segmentation task guide](../tasks/semantic_segmentation)

- [Monocular depth estimation task guide](../tasks/monocular_depth_estimation)

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## DPTConfig

[[autodoc]] DPTConfig

## DPTFeatureExtractor

[[autodoc]] DPTFeatureExtractor

    - __call__

    - post_process_semantic_segmentation

## DPTImageProcessor

[[autodoc]] DPTImageProcessor

    - preprocess

    - post_process_semantic_segmentation

## DPTModel

[[autodoc]] DPTModel

    - forward

## DPTForDepthEstimation

[[autodoc]] DPTForDepthEstimation

    - forward

## DPTForSemanticSegmentation

[[autodoc]] DPTForSemanticSegmentation

    - forward