No project description provided

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for huze from gravatar.com huze

Unverified details

These details have not been verified by PyPI
Meta

Project description

Documentation https://ncut-pytorch.readthedocs.io/

NCUT: Nyström Normalized Cut

Normalized Cut, aka. spectral clustering, is a graphical method to analyze data grouping in the affinity eigenvector space. It has been widely used for unsupervised segmentation in the 2000s.

Nyström Normalized Cut, is a new approximation algorithm developed for large-scale graph cuts, a large-graph of million nodes can be processed in under 10s (cpu) or 2s (gpu).

Gallery

TODO

Installation

PyPI install, our package is based on PyTorch, presuming you already have PyTorch installed

pip install ncut-pytorch

Install PyTorch if you haven't

pip install torch

Why NCUT

Normalized cut offers two advantages:

  1. soft-cluster assignments as eigenvectors

  2. hierarchical clustering by varying the number of eigenvectors

Please see NCUT and t-SNE/UMAP for a full comparison.

paper in prep, Yang 2024

AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space, Huzheng Yang, James Gee*, Jianbo Shi*, 2024

Normalized Cuts and Image Segmentation, Jianbo Shi and Jitendra Malik, 2000

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for huze from gravatar.com huze

Unverified details

These details have not been verified by PyPI
Meta

Release history Release notifications | RSS feed

1.8.9

1.8.8

1.8.7

1.8.6

1.8.5

1.8.4

1.8.3

1.8.2

1.8.1

1.8.0

1.7.9

1.7.8

1.7.7

1.7.6

1.7.5

1.7.4

1.7.3

1.7.2

1.7.1

1.7.0

1.6.13

1.6.12

1.6.11

1.6.10

1.6.9

1.6.8

1.6.7

1.6.6

1.6.5

1.6.4

1.6.3

1.6.2

1.6.1

1.6.0

1.5.4

1.5.3

1.5.2

1.5.1

1.5.0

1.4.4

1.4.3

1.4.2

1.4.1

1.4.0

1.3.16

1.3.15

1.3.14

1.3.13

1.3.12

1.3.11

1.3.10

1.3.9

1.3.8

1.3.7

1.3.6

1.3.5

1.3.4

1.3.3

1.3.2

1.3.1

1.3.0

1.2.7

1.2.6

1.2.5

1.2.4

1.2.3

1.2.2

1.2.1

1.2.0

1.1.0

1.0.12

1.0.11

1.0.10

1.0.9

This version

1.0.8

1.0.7

1.0.6

1.0.5

1.0.4

1.0.2

1.0.1

1.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ncut_pytorch-1.0.8.tar.gz (11.7 kB view details)

Uploaded Source

Built Distribution

ncut_pytorch-1.0.8-py3-none-any.whl (11.0 kB view details)

Uploaded Python 3

File details

Details for the file ncut_pytorch-1.0.8.tar.gz.

File metadata

  • Download URL: ncut_pytorch-1.0.8.tar.gz
  • Upload date:
  • Size: 11.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.8.16

File hashes

Hashes for ncut_pytorch-1.0.8.tar.gz
Algorithm Hash digest
SHA256 1b064f46bae94c3571051ae6686144e325d4231530f3242b74426aeb298ef158
MD5 4bff05f4b4d2622ca2174185fac04d7b
BLAKE2b-256 dad5eb79101a8db07fcb54e52fab834e99320fa8e0088141d6792eed5854d7eb

See more details on using hashes here.

File details

Details for the file ncut_pytorch-1.0.8-py3-none-any.whl.

File metadata

  • Download URL: ncut_pytorch-1.0.8-py3-none-any.whl
  • Upload date:
  • Size: 11.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.8.16

File hashes

Hashes for ncut_pytorch-1.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 6d6c4cbddb985287fc82f487e70b70578be1a7bbb5d1c8795fbc971050ea8e3a
MD5 416950f2d8c6843c18bd5d608e6cd748
BLAKE2b-256 03ded31aeab49ab8ac428d38d15f88b12115ffbffb819bb6174958b114c03ff4

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page