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dpt-large-ade

Image Segmentation
Transformers
PyTorch
dpt
vision
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dpt-large-ade
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---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- scene_parse_150
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
  example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
  example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
  example_title: Palace
---

# DPT (large-sized model) fine-tuned on ADE20k

Dense Prediction Transformer (DPT) model trained on ADE20k for semantic segmentation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/isl-org/DPT). 

Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for semantic segmentation.

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg)

## Intended uses & limitations

You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=dpt) to look for
fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model:

```python
from transformers import DPTFeatureExtractor, DPTForSemanticSegmentation
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade")
model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade")

inputs = feature_extractor(images=image, return_tensors="pt")

outputs = model(**inputs)
logits = outputs.logits
```

For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt).

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-2103-13413,
  author    = {Ren{\'{e}} Ranftl and
               Alexey Bochkovskiy and
               Vladlen Koltun},
  title     = {Vision Transformers for Dense Prediction},
  journal   = {CoRR},
  volume    = {abs/2103.13413},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.13413},
  eprinttype = {arXiv},
  eprint    = {2103.13413},
  timestamp = {Wed, 07 Apr 2021 15:31:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-13413.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```