PumaGuard
Introduction
Please visit http://pumaguard.rtfd.io/ for more information.
Get PumaGuard
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.
Local Development Environment
You can set up a local development environment using either uv (recommended for speed) or poetry.
Using uv (Recommended)
uv is an extremely fast Python package installer and resolver.
Install uv:
curl -LsSf https://astral.sh/uv/install.sh | sh
Or on Windows:
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
Create a virtual environment and install dependencies:
uv venv
source .venv/bin/activate # On Linux/macOS
# or
.venv\Scripts\activate # On Windows
# Install with development dependencies
uv pip install -e ".[dev,extra-dev]"
Or use uv sync for automatic environment management:
uv sync --extra dev --extra extra-dev
Using Poetry
Alternatively, you can use poetry:
sudo apt install python3-poetry
poetry install
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
Using uv:
uv run pumaguard-server FOLDER
Using poetry:
poetry run pumaguard-server FOLDER
Where FOLDER is the folder to watch.
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.
-
Get a TPU instance on Colab or run the script on your local machine.
-
Open a terminal and run
git clone https://github.com/PEEC-Nature-Youth-Group/pumaguard.git cd pumaguard
-
Get help on how to use the script
On Colab, run
./scripts/pumaguard --help ./scripts/pumaguard train --help
On your local machine with uv:
sudo apt install nvidia-cudnn uv sync --extra dev --extra extra-dev uv run pumaguard --help uv run pumaguard train --help
Or with poetry:
sudo apt install nvidia-cudnn poetry install poetry run pumaguard --help poetry run pumaguard train --help
-
Train the model from scratch
./scripts/pumaguard train --no-load --settings pumaguard-models/model_settings_6_pre-trained_512_512.yaml
1 maintainer
Metadata
1 maintainer
Release files for pumaguard 20.post97
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pumaguard-20.post97.tar.gz | 74.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pumaguard-20.post97-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 88.4 MB
Release files / pumaguard-20.post97.tar.gz
| Download URL | pumaguard-20.post97.tar.gz |
|---|---|
| Size | 74.8 MB |
| Tags | Source |
|
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Transparency logRelease files / pumaguard-20.post97-py3-none-any.whl
| Download URL | pumaguard-20.post97-py3-none-any.whl |
|---|---|
| Size | 13.6 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
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