castle-ai 0.0.14

Distinguish behavioral clusters Toolbox

CASTLE

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

Now we only support colab(with GPU) and Ubuntu 22.04 (with NVIDIA GPU).

Step 1 Install CASTLE Core Function

pip install torch==2.3.0+cu121 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

pip install castle-ai

Step 2 Install xFormer and GPU Version of UMAP (Optional for Speed Up)

For CUDA 12 Users

pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
pip install -U cudf-cu12 cuml-cu12 --extra-index-url=https://pypi.nvidia.com 

Step 3 Download Web UI

git clone https://github.com/CASTLE-ai/castle-ai.git

Step 4 Download Pretrained Model

cd castle-ai/
mkdir ckpt

Then, download the models from the web by copying the links to the Chrome browser and downloading them.

https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_reg4_pretrain.pth
https://drive.google.com/file/d/1g4E-F0RPOx9Nd6J7tU9AE1TjsouL4oZq/edit
https://drive.google.com/file/d/1QoChMkTVxdYZ_eBlZhK2acq9KMQZccPJ/edit

Afterward, place them into the ckpt folder.

castle-ai
├── castle
└── ckpt
    ├── dinov2_vitb14_reg4_pretrain.pth
    ├── R50_DeAOTL_PRE_YTB_DAV.pth
    ├── sam_vit_b_01ec64.pth
    └── SwinB_DeAOTL_PRE_YTB_DAV.pth

Run

python app.py

Reference

We would like to express our gratitude for the assistance received from the following projects during the development of this project.

Segment Anything

DeAOT & Segment-and-Track-Anything

DINOv2