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*This model was released on 2021-03-24 and added to Hugging Face Transformers on 2022-03-28.*

# DPT

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The DPT model was proposed in [Vision Transformers for Dense Prediction](https://huggingface.co/papers/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.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg"
alt="drawing" width="600"/>

<small> DPT architecture. Taken from the <a href="https://huggingface.co/papers/2103.13413" target="_blank">original paper</a>. </small>

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:

```python
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

## DPTImageProcessorFast

[[autodoc]] DPTImageProcessorFast
    - preprocess
    - post_process_semantic_segmentation
    - post_process_depth_estimation

## DPTModel

[[autodoc]] DPTModel
    - forward

## DPTForDepthEstimation

[[autodoc]] DPTForDepthEstimation
    - forward

## DPTForSemanticSegmentation

[[autodoc]] DPTForSemanticSegmentation
    - forward
