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metadata
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 by Ranftl et al. and first released in this repository.

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

Intended uses & limitations

You can use the raw model for semantic segmentation. See the model hub to look for fine-tuned versions on a task that interests you.

How to use

Here is how to use this model:

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.

BibTeX entry and citation info 

@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}
}