arXiv Open Access 2023

SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch

Zhenchao Jin

Lihat Sumber

Abstrak

This paper presents SSSegmenation, which is an open source supervised semantic image segmentation toolbox based on PyTorch. The design of this toolbox is motivated by MMSegmentation while it is easier to use because of fewer dependencies and achieves superior segmentation performance under a comparable training and testing setup. Moreover, the toolbox also provides plenty of trained weights for popular and contemporary semantic segmentation methods, including Deeplab, PSPNet, OCRNet, MaskFormer, \emph{etc}. We expect that this toolbox can contribute to the future development of semantic segmentation. Codes and model zoos are available at \href{https://github.com/SegmentationBLWX/sssegmentation/}{SSSegmenation}.

Topik & Kata Kunci

cs.CV

Penulis (1)

Zhenchao Jin

Format Sitasi

APA MLA BibTeX

Jin, Z. (2023). SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch. https://arxiv.org/abs/2305.17091

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓