Welcome to deepvisiontools' official documentation

release: 1.0.2

Installation

It is recommended to install deepvisiontools on a GPU-enabled machine/server. We suggest that you use a Python virtual environment (uv, conda, venv, etc.) with Python version between 3.10 and 3.14.

deepvisiontools is available on PyPI, so you can simply run in a Python environment:

pip install deepvisiontools

To install deepvisiontools in development mode, please clone the repository and run:

pip install -e deepvisiontools/ # local cloned repo

Documentation and tutorials

To get started with deepvisiontools, after installation you can run:

deepvisiontools download-tutorials

You will also need to download the demo datasets:

deepvisiontools download-demo-datasets

The first tutorial, getting_started, takes about 1 hour and explains all the basics of deepvisiontools. From there you have all information to get started with simple tasks.

We then recommend moving to advanced_usage to get a broader view of what you can configure in deepvisiontools and all available helpers. In particular it covers: - Custom global config - TrainerConfig in details: EMA, LLR, scheduler etc. - Custom datasets - Implement your own models

Finally, the hyper_parametrs_optimization tutorial will explain the usage of Optuna wrappers available in deepvisiontools to perform extensive parameter space searches.