seamseg
Research code for panoptic segmentation, which labels every pixel of an image by class and by individual object. It is a neural network architecture from a CVPR paper.
Share on XLicense: BSD-3-Clause
Overview
Seamless Scene Segmentation is a CNN-based architecture for panoptic segmentation, which gives every pixel of an image both a class label and an instance label. It combines multi-scale features from a Feature Pyramid Network with context from a light DeepLab-like module. The repository holds PyTorch training and evaluation code, built on a re-implementation of Mask R-CNN, and comes from a CVPR 2019 paper.
Key features
- Panoptic segmentation with class and instance labels per pixel
- Segmentation head joining Feature Pyramid Network features with DeepLab-like context
- PyTorch training and evaluation code
- Based on a re-implementation of Mask R-CNN
Best for
Researchers who want to train or evaluate panoptic segmentation. The stated setup is CUDA 10.1, Linux with GCC 7 or 8, and PyTorch 1.1.0.
- Upstream
- mapillary/seamseg
- Fork on GitHub
- Guo-astro/seamseg
- Upstream stars
- 301
- Category
- Finance, data and research
- Language
- Python
- License
- BSD-3-Clause
- Forked
- 2020-12-05
- Sync status
- In syncLast synced 2026-09-29
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