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

This version

1.0.9

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-1.0.9.tar.gz (11.7 kB view details)

Uploaded Source

Built Distribution

ncut_pytorch-1.0.9-py3-none-any.whl (11.0 kB view details)

Uploaded Python 3

File details

Details for the file ncut_pytorch-1.0.9.tar.gz.

File metadata

  • Download URL: ncut_pytorch-1.0.9.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-1.0.9.tar.gz
Algorithm Hash digest
SHA256 90766b6587122ea03ebcfa5ff13608240d3a140076c5ae0b4b7e5d17eb9238bb
MD5 c7710799c4aeacace4ad845503749cd9
BLAKE2b-256 41e1cc806a26cb694f076d2606ed29a81a4f06d1c0e2951d1d81cf62ec3087aa

See more details on using hashes here.

File details

Details for the file ncut_pytorch-1.0.9-py3-none-any.whl.

File metadata

  • Download URL: ncut_pytorch-1.0.9-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-1.0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 875c901656a331cb42e6e16589c4373c4fae7407a7099af49ad13017ec692e8c
MD5 3fb217bd9599280a2df20c0a33b325a9
BLAKE2b-256 4ed71d2b49432b85f6ab688ef4b5a488f6b19dbf4122e55c35362f5a191c2bf5

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