Skip to main content

PumaGuard

Build and Test Webpage

Test and package code

Open in GitHub Codespaces

Open in Colab

Introduction

Please visit http://pumaguard.rtfd.io/ for more information.

Get PumaGuard

PyPI - Version

GitHub Codespaces

If you do not want to install any new software on your computer you can use GitHub Codespaces, which provide a development environment in your browser.

Open in GitHub Codespaces

Local Development Environment

A local development environment can be created by using the poetry tool, which can be installed with

sudo apt install python3-poetry

Run

poetry install

To install all of the necessary Python modules.

Running the scripts on colab.research.google.com

Google Colab offers runtimes with GPUs and TPUs, which make training a model much faster. In order to run the training script in Google Colab, do the following from the terminal:

git clone https://github.com/PEEC-Nature-Youth-Group/pumaguard.git
cd pumaguard
scripts/train.py --help

For example, if you want to train the model from row 1 in the notebook,

scripts/train.py --notebook 1

Running the server

The pumaguard-server watches a folder and classifies new files as they are added to that folder. Run with

poetry run pumaguard-server FOLDER

Where FOLDER is the folder to watch.

Server Demo Session

Training new models

For reproducibility, training new models should be done via the train script and all necessary data, i.e. images, and the resulting weights and history should be committed to the repository.

  1. Get a TPU instance on Colab or run the script on your local machine.

  2. Open a terminal and run

    git clone https://github.com/PEEC-Nature-Youth-Group/pumaguard.git
    cd pumaguard
    
  3. Get help on how to use the script

    On Colab, run

    ./scripts/pumaguard --help
    ./scripts/pumaguard train --help
    

    On your local machine, run

    sudo apt install nvidia-cudnn
    poetry install
    poetry run pumaguard --help
    poetry run pumaguard train --help
    
  4. Train the model from scratch

    ./scripts/pumaguard train --no-load --settings pumaguard-models/model_settings_6_pre-trained_512_512.yaml
    

Metadata

Release files for pumaguard 18.post40

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for pumaguard 18.post40
File Interpreter ABI Platform
pumaguard-18.post40-py3-none-any.whl Python 3 none any Details

Release files / pumaguard-18.post40-py3-none-any.whl

Download URL pumaguard-18.post40-py3-none-any.whl
Size 93.2 MB
Tags Python 3
SHA-256 checksum
How to use checksums
3aeef187737678071bc72822f036af37b009a85491f4b9eb2989cdd4eda895a0
BLAKE2b-256 checksum
How to use checksums
bb4b7dd362962d4e7af0f97560990c3c89c0c1be28a60b97dc11b717639d99a7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 23, 2025.

Transparency log

Release history Release notifications | RSS feed

25

1 release file

24

1 release file

23

1 release file

21

2 release files

20

2 release files

This release

18.post40 This release

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page