91 lines
4.6 KiB
Markdown
91 lines
4.6 KiB
Markdown
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
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
|
|
specific language governing permissions and limitations under the License.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
|
|
# 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.*
|
|
|
|
<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://arxiv.org/abs/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
|
|
- post_process_semantic_segmentation
|
|
|
|
## DPTModel
|
|
|
|
[[autodoc]] DPTModel
|
|
- forward
|
|
|
|
## DPTForDepthEstimation
|
|
|
|
[[autodoc]] DPTForDepthEstimation
|
|
- forward
|
|
|
|
## DPTForSemanticSegmentation
|
|
|
|
[[autodoc]] DPTForSemanticSegmentation
|
|
- forward
|