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dpt-large-ade

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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

The model is used for semantic segmentation of input images such as seen in the table below:

Input Image Output Segmented Image
input image Segmented image

Model description

The Midas 3.0 nbased Dense Prediction Transformer (DPT) model was 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.

The MiDaS v3.0 DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for semantic segmentation.

model image

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 and the Intel AI Community team.

Results:

According to the authors, at the time of publication, 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. Our models are available at Intel DPT GItHub Repository.

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/val2017/000000026204.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = DPTImageProcessor .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
print(logits.shape)
logits
prediction = torch.nn.functional.interpolate(
    logits,
    size=image.size[::-1],  # Reverse the size of the original image (width, height)
    mode="bicubic",
    align_corners=False
)

# Convert logits to class predictions
prediction = torch.argmax(prediction, dim=1) + 1

# Squeeze the prediction tensor to remove dimensions
prediction = prediction.squeeze()

# Move the prediction tensor to the CPU and convert it to a numpy array
prediction = prediction.cpu().numpy()

# Convert the prediction array to an image
predicted_seg = Image.fromarray(prediction.squeeze().astype('uint8'))

# Define the ADE20K palette
adepallete = [0,0,0,120,120,120,180,120,120,6,230,230,80,50,50,4,200,3,120,120,80,140,140,140,204,5,255,230,230,230,4,250,7,224,5,255,235,255,7,150,5,61,120,120,70,8,255,51,255,6,82,143,255,140,204,255,4,255,51,7,204,70,3,0,102,200,61,230,250,255,6,51,11,102,255,255,7,71,255,9,224,9,7,230,220,220,220,255,9,92,112,9,255,8,255,214,7,255,224,255,184,6,10,255,71,255,41,10,7,255,255,224,255,8,102,8,255,255,61,6,255,194,7,255,122,8,0,255,20,255,8,41,255,5,153,6,51,255,235,12,255,160,150,20,0,163,255,140,140,140,250,10,15,20,255,0,31,255,0,255,31,0,255,224,0,153,255,0,0,0,255,255,71,0,0,235,255,0,173,255,31,0,255,11,200,200,255,82,0,0,255,245,0,61,255,0,255,112,0,255,133,255,0,0,255,163,0,255,102,0,194,255,0,0,143,255,51,255,0,0,82,255,0,255,41,0,255,173,10,0,255,173,255,0,0,255,153,255,92,0,255,0,255,255,0,245,255,0,102,255,173,0,255,0,20,255,184,184,0,31,255,0,255,61,0,71,255,255,0,204,0,255,194,0,255,82,0,10,255,0,112,255,51,0,255,0,194,255,0,122,255,0,255,163,255,153,0,0,255,10,255,112,0,143,255,0,82,0,255,163,255,0,255,235,0,8,184,170,133,0,255,0,255,92,184,0,255,255,0,31,0,184,255,0,214,255,255,0,112,92,255,0,0,224,255,112,224,255,70,184,160,163,0,255,153,0,255,71,255,0,255,0,163,255,204,0,255,0,143,0,255,235,133,255,0,255,0,235,245,0,255,255,0,122,255,245,0,10,190,212,214,255,0,0,204,255,20,0,255,255,255,0,0,153,255,0,41,255,0,255,204,41,0,255,41,255,0,173,0,255,0,245,255,71,0,255,122,0,255,0,255,184,0,92,255,184,255,0,0,133,255,255,214,0,25,194,194,102,255,0,92,0,255]

# Apply the color map to the predicted segmentation image
predicted_seg.putpalette(adepallete)

# Blend the original image and the predicted segmentation image
out = Image.blend(image, predicted_seg.convert("RGB"), alpha=0.5)

out

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}
}