Metadata-Version: 2.1
Name: castle-ai
Version: 0.0.2
Summary: Distinguish behavioral clusters Toolbox
Maintainer: Raiso Liu
Maintainer-email: rainsoon717@gmail.com
License: AGPL-3.0 license
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: torch
Requires-Dist: av
Requires-Dist: opencv-python
Requires-Dist: torchvision
Requires-Dist: matplotlib

# CASTLE


[![PyPI version](https://badge.fury.io/py/castle-ai.svg)](https://badge.fury.io/py/castle-ai)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](???)

CASTLE integrates the strengths of visual foundation models trained on large datasets possess open-world visual concepts, including Segment Anything (SA), DeAOT, and DINOv2, for one-shot image segmentation and unsupervised visual feature extraction. Furthermore, CASTLE employs unsupervised multi-stage and/or multi-layer UMAP clustering algorithms to distinguish behavioral clusters with unique temporal variability. 

# Install
```
pip install castle-ai
```

# Reference

[Segment Anything](https://github.com/facebookresearch/segment-anything.git)
[DeAOT](https://github.com/z-x-yang/Segment-and-Track-Anything.git)
[DINOv2](https://github.com/facebookresearch/dinov2.git)
