许可证：apache-2.0
标签：

• 视觉
• 图像分割
  数据集：
• scene_parse_150
  示例展示：
• 图片链接：https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
  示例标题：老虎
• 图片链接：https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
  示例标题：茶壶
• 图片链接：https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
  示例标题：宫殿

基于ADE20k微调的DPT（大尺寸模型）

该模型用于输入图像的语义分割，如下表所示：

输入图像	输出分割图像

模型描述

基于Midas 3.0的密集预测变换器（DPT）模型在ADE20k数据集上进行了语义分割训练。该模型由Ranftl等人在论文《Vision Transformers for Dense Prediction》中提出，并首次发布于此代码库。

MiDaS v3.0 DPT使用视觉变换器（ViT）作为主干网络，并在其基础上添加了用于语义分割的颈部结构和头部结构。

免责声明：发布DPT的团队未为此模型编写模型卡，因此本模型卡由Hugging Face和英特尔AI社区团队编写。

结果：

根据作者的说法，在发布时，当应用于语义分割时，密集视觉变换器在ADE20K上以49.02%的mIoU创造了新的最先进水平。

我们进一步展示了该架构可以在较小的数据集（如NYUv2、KITTI和Pascal Context）上进行微调，并在这些数据集上也达到了新的最先进水平。我们的模型可在英特尔DPT GitHub仓库中找到。

预期用途与限制

您可以将原始模型用于语义分割。请参阅模型中心以查找您感兴趣任务的微调版本。

使用方法

以下是使用该模型的方法：

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],  # 反转原始图像的尺寸（宽度，高度）
    mode="bicubic",
    align_corners=False
)

# 将logits转换为类别预测
prediction = torch.argmax(prediction, dim=1) + 1

# 压缩预测张量以去除维度
prediction = prediction.squeeze()

# 将预测张量移至CPU并转换为numpy数组
prediction = prediction.cpu().numpy()

# 将预测数组转换为图像
predicted_seg = Image.fromarray(prediction.squeeze().astype('uint8'))

# 定义ADE20K调色板
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]

# 将颜色映射应用于预测的分割图像
predicted_seg.putpalette(adepallete)

# 将原始图像与预测的分割图像混合
out = Image.blend(image, predicted_seg.convert("RGB"), alpha=0.5)

out

更多代码示例，请参阅文档。

BibTeX条目和引用信息

@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}
}