Onda
Source code for "Online Unsupervised Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions", ECCV 2022. This is the code has been implemented to perform training and evaluation of UDA approaches in continuous scenarios. The library has been implemented in PyTorch 1.7.1. Some newer versions should work as well.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Dassl.pytorch | 681 | 2 years ago | 9 | mit | Python | |||||
| A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning. | ||||||||||
| Da | 630 | 3 years ago | 4 | mit | ||||||
| Unsupervised Domain Adaptation Papers and Code | ||||||||||
| Adaptsegnet | 586 | 4 years ago | 13 | Python | ||||||
| Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight) | ||||||||||
| Deep Transfer Learning | 517 | 2 years ago | mit | Python | ||||||
| A collection of implementations of deep domain adaptation algorithms | ||||||||||
| Powerful Benchmarker | 426 | 3 months ago | 34 | September 19, 2020 | 3 | Jupyter Notebook | ||||
| A library for ML benchmarking. It's powerful. | ||||||||||
| Pykale | 415 | 3 months ago | 12 | April 12, 2022 | 9 | mit | Python | |||
| Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work! | ||||||||||
| Eanet | 374 | 3 years ago | 19 | Python | ||||||
| EANet: Enhancing Alignment for Cross-Domain Person Re-identification | ||||||||||
| Seg Uncertainty | 374 | 3 months ago | 9 | mit | Python | |||||
| IJCAI2020 & IJCV2021 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo | ||||||||||
| Dann_py3 | 298 | 2 years ago | 2 | mit | Python | |||||
| python 3 pytorch implementation of DANN | ||||||||||
| Dann | 295 | 5 years ago | mit | Python | ||||||
| pytorch implementation of Domain-Adversarial Training of Neural Networks | ||||||||||
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