模型:
nvidia/segformer-b0-finetuned-cityscapes-640-1280
SegFormer模型在分辨率为640x1280的CityScapes数据集上进行了精调。该模型在Xie等人的论文 SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers 中提出并首次发布于 this repository 。
免责声明:SegFormer发布团队并未为该模型撰写模型卡,因此本模型卡是由Hugging Face团队编写的。
SegFormer由一个分层Transformer编码器和一个轻量级全MLP解码头组成,可在ADE20K和CityScapes等语义分割基准上取得出色的结果。分层Transformer首先在ImageNet-1k上进行预训练,然后添加解码头并在下游数据集上进行整体微调。
您可以使用原始模型进行语义分割。请查看 model hub ,寻找您感兴趣的任务的精调版本。
以下是使用该模型将COCO 2017数据集中的图像分类为1,000个ImageNet类别之一的方法:
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280") model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280") url = "http://images.cocodataset.org/val2017/000000039769.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)
有关更多代码示例,请参考 documentation 。
有关该模型的许可证信息,请参见 here 。
@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} }