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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.
## 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}
}
``` |