CSAILVision/semantic-segmentation-pytorch
A PyTorch implementation of semantic segmentation models trained on the MIT ADE20K scene parsing dataset.
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This repository provides PyTorch implementations of semantic segmentation models for scene parsing on the MIT ADE20K dataset, which is the largest open-source dataset for semantic segmentation. The code includes training pipelines, pre-trained model weights, and a synchronized batch normalization module that computes statistics across multiple devices for improved segmentation accuracy. Users can run inference via a web demo, a Colab notebook, or by loading models programmatically.
Frequently asked
- What is CSAILVision/semantic-segmentation-pytorch?
- A PyTorch implementation of semantic segmentation models trained on the MIT ADE20K scene parsing dataset.
- Is semantic-segmentation-pytorch open source?
- Yes — CSAILVision/semantic-segmentation-pytorch is open source, released under the BSD-3-Clause license.
- What language is semantic-segmentation-pytorch written in?
- CSAILVision/semantic-segmentation-pytorch is primarily written in Python.
- How popular is semantic-segmentation-pytorch?
- CSAILVision/semantic-segmentation-pytorch has 5.1k stars on GitHub.
- Where can I find semantic-segmentation-pytorch?
- CSAILVision/semantic-segmentation-pytorch is on GitHub at https://github.com/CSAILVision/semantic-segmentation-pytorch.