update README

README.md

@@ -12,42 +12,4 @@ license: apache-2.0
 
 Documentation [https://ncut-pytorch.readthedocs.io/](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](https://pytorch.org/get-started/locally/), presuming you already have PyTorch installed
-
-```shell
-pip install ncut-pytorch
-```
-
-[Install PyTorch](https://pytorch.org/get-started/locally/) if you haven't
-```shell
-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](compare.md) 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
-> 
+We thank the Hugging Face team for providing free GPU for hosting this demo.