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Segmenter is a PyTorch implementation of a transformer-based model for semantic segmentation, presented at ICCV 2021. It supports different backbones, datasets, and evaluation metrics, and provides scripts for inference, training, and visualizing attention maps.
Segmenter is a transformer model for image segmentation that uses global context and pre-trained image classification models. It outperforms the state of the art on ADE20K and Pascal Context datasets and is competitive on Cityscapes.
Segmenter是完全基于transformer的编码器-解码器架构，将一系列patch嵌入映射到像素级类标注。该模型的概述如图2所示。patch序列由3.1节中描述的transformer编码器编码，并由3.2节中描述的逐点线性映射或掩码transformer解码。 ...
Segmenter is a transformer model that leverages global context for image segmentation. It outperforms state-of-the-art convolutional methods on challenging datasets and allows to trade-off between accuracy and speed.
Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a transformer model for semantic segmentation. In contrast to convolution-based methods, our approach allows to model global context already at the first layer and throughout the network. We build on the recent Vision ...
This paper introduces Segmenter, a transformer model for semantic segmentation that outperforms the state of the art on both ADE20K and Pascal Context datasets and is competitive on Cityscapes. Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a transformer model for ...
Segmenter attains excellent results for semantic segmentation. It outperforms the state of the art on both ADE20K and Pascal Context datasets and is competitive on Cityscapes. PDF Abstract ICCV 2021 PDF ICCV 2021 Abstract. Code Edit Add Remove Mark official. rstrudel/segmenter ...
Segmenter is a transformer model that leverages global context for image segmentation. It outperforms state-of-the-art convolutional methods on ADE20K, Pascal Context and Cityscapes datasets.
Segmenter attains excellent results for semantic segmentation. It outperforms the state of the art on both ADE20K and Pascal Context datasets and is competitive on Cityscapes. Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce ...
Segmenter is a transformer model that uses global context to segment images. It outperforms the state of the art on ADE20K, Pascal Context and Cityscapes datasets.