Unsupervised-Semantic-Segmentation
PASS
| Unsupervised-Semantic-Segmentation | PASS | |
|---|---|---|
| 1 | 4 | |
| 386 | 257 | |
| - | - | |
| 1.8 | 0.0 | |
| almost 2 years ago | almost 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Unsupervised-Semantic-Segmentation
-
Unsupervised semantic segmentation
Check out these unsupervised masks created in exactly such way in this paper. They are nearly perfect
PASS
-
[D] Does anyone know a large varied image dataset that do NOT contain humans?
Here is what you are looking for https://github.com/yukimasano/PASS
- PASS
- Pass: A large-scale image dataset that doesn't include any humans
-
University of Oxford Researchers Release ‘PASS’ Dataset With 1.4M+ Images (Free From Humans) For Self-Supervised Machine Learning
3 Min Read | Paper | Project | Code
What are some alternatives?
mmselfsup - OpenMMLab Self-Supervised Learning Toolbox and Benchmark
auth-source-pass - Integrate Emacs' auth-source with password-store
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
DA-RetinaNet - Official Detectron2 implementation of DA-RetinaNet of our Image and Vision Computing 2021 work 'An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites'
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
solo-learn - solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
self-supervised - Whitening for Self-Supervised Representation Learning | Official repository
DiffCSE - Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
lightly - A python library for self-supervised learning on images.