An open-source machine learning tool for behavioral segmentation and analyses.
Project description
VAME App
A web application for the Variational Animal Motion Encoding (VAME) project — an open-source machine learning tool for behavioral segmentation and analyses.
VAME App runs a Python (Flask) backend which calls the VAME library and serves a React frontend, which you use in your browser.
Requirements
- Python ≥ 3.12
- ffmpeg (needed for some video/image functions)
- A modern browser (Chrome, Edge, Firefox, or Safari)
Install
pip install vame-app
If PyTorch fails to install for your platform, install it first following the official instructions, then re-run
pip install vame-app.
Run
vame-app
This starts the local server and opens the app in your browser.
CLI options
vame-app [--host HOST] [--port PORT] [--data-root DIR] [--no-browser] [--dev]
| Flag | Default | Purpose |
|---|---|---|
--host |
127.0.0.1 |
Interface to bind. Use 0.0.0.0 to expose on the LAN. |
--port |
8641 |
Port to listen on. Use 0 to auto-pick a free port. |
--data-root |
home directory | Root the in-app file browser may traverse. Restrict this on shared servers. |
--no-browser |
off | Don't auto-open the browser. |
--dev |
off | Use the Flask dev server instead of waitress. |
Projects are stored under ~/vame-app/projects. --data-root (env
VAME_DATA_ROOT) only controls the file browser root, not project storage.
Development
Contributing or running from source? See README-DEV.md.
Project details
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The following attestation bundles were made for vame_app-0.4.2-py3-none-any.whl:
Publisher:
build_and_publish.yml on EthoML/vame-app
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https://in-toto.io/Statement/v1 -
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EthoML/vame-app@71fbca869749392fb75b71767980464abe00b297 -
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