yasserben/CLOUDS
[CVPR 2024] Official Implementation of Collaborating Foundation models for Domain Generalized Semantic Segmentation
This project helps computer vision researchers and practitioners build robust image segmentation models that work well in new, previously unseen environments. It takes a labeled dataset from a "source domain" (e.g., images from a specific city or weather condition) and trains a model that can accurately segment objects in images from entirely different, unlabeled "target domains" (e.g., different cities, varying weather). This is ideal for those needing to deploy vision systems in diverse, unpredictable real-world settings.
No commits in the last 6 months.
Use this if you need an image segmentation model trained on one type of data to perform accurately across many different, unencountered visual conditions.
Not ideal if your image segmentation tasks always occur within highly controlled, consistent visual environments.
Stars
76
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 04, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/yasserben/CLOUDS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepinv/deepinv
DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning
fidler-lab/polyrnn-pp
Inference Code for Polygon-RNN++ (CVPR 2018)
mhamilton723/STEGO
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
yjxiong/tsn-pytorch
Temporal Segment Networks (TSN) in PyTorch
pyxu-org/pyxu
Modular and scalable computational imaging in Python with GPU/out-of-core computing.