arthurdouillard/CVPR2021_PLOP
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation
This project helps computer vision engineers and researchers improve how machine learning models learn to identify different objects in images over time. It takes labeled image datasets and existing models, and outputs a refined model that can recognize new objects without forgetting previously learned ones. The primary users are those working on evolving computer vision systems, such as in robotics or autonomous driving.
156 stars. No commits in the last 6 months.
Use this if you need to continuously update a semantic segmentation model to recognize new categories of objects in images without degrading its performance on categories it already knows.
Not ideal if you are looking for a pre-trained, ready-to-use model for semantic segmentation without needing to train it on evolving datasets.
Stars
156
Forks
23
Language
Python
License
MIT
Category
Last pushed
Feb 09, 2022
Commits (30d)
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