πŸ‘©β€πŸ’» User guide

πŸ“š Contents:

⬇️ Installation

Scivision is a Python package. You can install the latest version from PyPi with pip:

pip install scivision

πŸ§ͺ Getting started

Scivision enables users to quickly find computer vision models that can be run on matching scientific image dataset(s), or find datasets that models can be run on. This can be achieved by running several lines of Python code.

The core functionality of the Python package API is documented in the 🌟 Scivision API documentation and a Jupyter notebook demonstrating it can be found on GitHub or can be interactively run via clicking the following link to Binder:

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In brief, the notebook demonstrates the following:

  1. Loading a pretrained (ImageNet) model, which was previously added to the scivision catalog with the name β€œscivision-test-plugin”

    model = load_pretrained_model('/path/to/model/repo')

  2. Using the β€œdefault” scivision catalog to find a matching dataset, which the loaded model can be run on

    default_catalog.compatible_datasources(<model name>)

  3. Loading the dataset in a format the model can recognise

    load_dataset('/path/to/data/repo')

  4. Running the model on the data, performing simple model inference

    model.predict(<loaded data>)

πŸ“– Catalog of models and datasets

The models and datasets you find when searching the β€œdefault” scivision catalog (that which comes with the package) are loaded from external sources via metadata included in the GitHub repository.

To understand how these external sources are configured for compatibility with scivision, consult the following sections of this documentation:

To understand how to contribute new models and data sources to the scivision catalog, check out: