HRNet/HRNet-Semantic-Segmentation
PyTorch implementation of HRNet with OCR for semantic segmentation achieving state-of-the-art results on Cityscapes, PASCAL-Context, and other benchmarks.
Not currently ranked — collecting fresh signals.
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This repository provides an official implementation of High-Resolution Networks (HRNet) combined with Object-Contextual Representations (OCR), rephrased as the Segmentation Transformer. The project targets semantic segmentation, a fundamental computer vision task that assigns class labels to each pixel in an image. It includes models trained on standard benchmarks including Cityscapes, PASCAL-Context, LIP, and ADE20K, and has been integrated into the MMSegmentation framework.
Frequently asked
- What is HRNet/HRNet-Semantic-Segmentation?
- PyTorch implementation of HRNet with OCR for semantic segmentation achieving state-of-the-art results on Cityscapes, PASCAL-Context, and other benchmarks.
- Is HRNet-Semantic-Segmentation open source?
- Yes — HRNet/HRNet-Semantic-Segmentation is an open-source project tracked on heatdrop.
- What language is HRNet-Semantic-Segmentation written in?
- HRNet/HRNet-Semantic-Segmentation is primarily written in Python.
- How popular is HRNet-Semantic-Segmentation?
- HRNet/HRNet-Semantic-Segmentation has 3.3k stars on GitHub.
- Where can I find HRNet-Semantic-Segmentation?
- HRNet/HRNet-Semantic-Segmentation is on GitHub at https://github.com/HRNet/HRNet-Semantic-Segmentation.