Dpt Large Ade

🚀 DPT（大型模型）在ADE20k上微調

本模型用於輸入圖像的語義分割，如下表所示：

輸入圖像	輸出分割圖像

✨ 主要特性

• 基於Midas 3.0的密集預測Transformer（DPT）模型，在ADE20k上進行語義分割訓練。
• 使用視覺Transformer（ViT）作為骨幹網絡，並在頂部添加了頸部和頭部用於語義分割。
• 在語義分割任務中，密集視覺變換器在ADE20K上達到了49.02%的mIoU，創造了新的技術水平。
• 該架構可以在較小的數據集（如NYUv2、KITTI和Pascal Context）上進行微調，並創造新的技術水平。

📚 詳細文檔

模型描述

基於Midas 3.0的密集預測Transformer（DPT）模型在ADE20k上進行了語義分割訓練。該模型由Ranftl等人在論文 Vision Transformers for Dense Prediction 中提出，並首次在 此倉庫 發佈。

MiDaS v3.0 DPT使用視覺Transformer（ViT）作為骨幹網絡，並在頂部添加了頸部和頭部用於語義分割。

免責聲明：發佈DPT的團隊沒有為此模型編寫模型卡片，因此此模型卡片由Hugging Face和英特爾AI社區團隊編寫。

結果

根據作者的說法，在發佈時，當應用於語義分割時，密集視覺變換器在 ADE20K上以49.02%的mIoU創造了新的技術水平。

我們進一步證明，該架構可以在較小的數據集（如NYUv2、KITTI和Pascal Context）上進行微調，並且也創造了新的技術水平。我們的模型可在 英特爾DPT GitHub倉庫 找到。

預期用途和限制

你可以使用原始模型進行語義分割。請查看 模型中心 以查找針對你感興趣的任務進行微調的版本。

💻 使用示例

基礎用法

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

更多代碼示例，請參考 文檔。

BibTeX引用和引用信息

@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}
}

📄 許可證

本項目採用Apache-2.0許可證。