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

This version

1.0.10

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-1.0.10.tar.gz (11.7 kB view details)

Uploaded Source

Built Distribution

ncut_pytorch-1.0.10-py3-none-any.whl (11.0 kB view details)

Uploaded Python 3

File details

Details for the file ncut_pytorch-1.0.10.tar.gz.

File metadata

  • Download URL: ncut_pytorch-1.0.10.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-1.0.10.tar.gz
Algorithm Hash digest
SHA256 f5388fe6b9772eab0c152949b6baf40b3b405f019fab303981117393a4990800
MD5 1277e2baff85efaf68aaf66946a1a7e0
BLAKE2b-256 81d36280cd4393d509e98d6c415a58d451945b07b116111d49d7a4f955eb018a

See more details on using hashes here.

File details

Details for the file ncut_pytorch-1.0.10-py3-none-any.whl.

File metadata

File hashes

Hashes for ncut_pytorch-1.0.10-py3-none-any.whl
Algorithm Hash digest
SHA256 33c4e9e05500b9d3fda5d8658fd08000a01b3eab9cd22bb25f21c7071a590b6e
MD5 ee3847547f5b3e65ecd2410979103978
BLAKE2b-256 aeeefef3e75ef3c09a370319ecfb277793b39cc07581efbf1451a156f7ece402

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