Segformer B0 Finetuned Segments Sidewalk 2

🚀 SegFormer (b0尺寸) 模型在Segments.ai人行道语义数据集上的微调

本项目是在 Segments.ai 的 sidewalk-semantic 数据集上微调的 SegFormer 模型。该模型由 Xie 等人在论文 SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers 中提出，并首次在 此仓库 发布。它可用于解决人行道图像语义分割问题，为相关领域的研究和应用提供了有效的工具。

🚀 快速开始

SegFormer 由一个分层 Transformer 编码器和一个轻量级全 MLP 解码头组成，能在 ADE20K 和 Cityscapes 等语义分割基准测试中取得出色的结果。分层 Transformer 首先在 ImageNet - 1k 上进行预训练，然后添加一个解码头，并在下游数据集上进行整体微调。

💻 使用示例

基础用法

以下是如何使用此模型对人行道数据集的图像进行分类的示例：

from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
from PIL import Image
import requests
feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = SegformerForSemanticSegmentation.from_pretrained("segments-tobias/segformer-b0-finetuned-segments-sidewalk")
url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)

如需更多代码示例，请参考 文档。

📚 详细文档

BibTeX 引用和引用信息

@article{DBLP:journals/corr/abs-2105-15203,
  author    = {Enze Xie and
               Wenhai Wang and
               Zhiding Yu and
               Anima Anandkumar and
               Jose M. Alvarez and
               Ping Luo},
  title     = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
               Transformers},
  journal   = {CoRR},
  volume    = {abs/2105.15203},
  year      = {2021},
  url       = {https://arxiv.org/abs/2105.15203},
  eprinttype = {arXiv},
  eprint    = {2105.15203},
  timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

📦 模型信息