ncut-pytorch 1.1.0

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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).

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Installation & Quick Start

PyPI install, our package is based on PyTorch, please install PyTorch first

pip install ncut-pytorch

Minimal example on how to run NCUT, more examples in Documentation.

import torch
from ncut_pytorch import NCUT, rgb_from_tsne_3d

model_features = torch.rand(20, 64, 64, 768)

inp = model_features.reshape(-1, 768)  # flatten
eigvectors, eigvalues = NCUT(num_eig=100, device='cuda:0').fit_transform(inp)
tsne_x3d, tsne_rgb = rgb_from_tsne_3d(eigvectors, device='cuda:0')

eigvectors = eigvectors.reshape(20, 64, 64, 100)
tsne_rgb = tsne_rgb.reshape(20, 64, 64, 3)

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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