https://github.com/artemmavrin/focal-loss

TensorFlow implementation of focal loss
https://github.com/artemmavrin/focal-loss

deep-learning keras loss-functions tensorflow

Last synced: about 1 month ago
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TensorFlow implementation of focal loss

• Host: GitHub
• URL: https://github.com/artemmavrin/focal-loss
• Owner: artemmavrin
• License: apache-2.0
• Archived: true
• Created: 2019-09-21T05:54:54.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-05-28T19:04:19.000Z (over 3 years ago)
• Last Synced: 2024-09-19T10:08:45.538Z (about 2 months ago)
• Topics: deep-learning, keras, loss-functions, tensorflow
• Language: Python
• Homepage: https://focal-loss.readthedocs.io
• Size: 177 KB
• Stars: 186
• Watchers: 3
• Forks: 43
• Open Issues: 6
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
==========

Focal Loss

==========

.. image:: https://img.shields.io/pypi/pyversions/focal-loss

    :target: https://pypi.org/project/focal-loss

    :alt: Python Version

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    :target: https://pypi.org/project/focal-loss

    :alt: PyPI Package Version

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    :target: https://codecov.io/gh/artemmavrin/focal-loss

    :alt: Code Coverage

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    :target: https://github.com/artemmavrin/focal-loss/blob/master/LICENSE

    :alt: License

TensorFlow implementation of focal loss [1]_: a loss function generalizing

binary and multiclass cross-entropy loss that penalizes hard-to-classify

examples.

The ``focal_loss`` package provides functions and classes that can be used as

off-the-shelf replacements for ``tf.keras.losses`` functions and classes,

respectively.

.. code-block:: python

    # Typical tf.keras API usage

    import tensorflow as tf

    from focal_loss import BinaryFocalLoss

    model = tf.keras.Model(...)

    model.compile(

        optimizer=...,

        loss=BinaryFocalLoss(gamma=2),  # Used here like a tf.keras loss

        metrics=...,

    )

    history = model.fit(...)

The ``focal_loss`` package includes the functions

* ``binary_focal_loss``

* ``sparse_categorical_focal_loss``

and wrapper classes

* ``BinaryFocalLoss`` (use like ``tf.keras.losses.BinaryCrossentropy``)

* ``SparseCategoricalFocalLoss`` (use like ``tf.keras.losses.SparseCategoricalCrossentropy``)

Documentation is available at

`Read the Docs `__.

.. image:: docs/source/images/focal-loss.png

    :alt: Focal loss plot

Installation

------------

The ``focal_loss`` package can be installed using the

`pip `__ utility.

For the latest version, install directly from the package's

`GitHub page `__:

.. code-block:: bash

    pip install git+https://github.com/artemmavrin/focal-loss.git

Alternatively, install a recent release from the

`Python Package Index (PyPI) `__:

.. code-block:: bash

    pip install focal-loss

**Note.** To install the project for development (e.g., to make changes to

the source code), clone the project repository from GitHub and run

:code:`make dev`:

.. code-block:: bash

    git clone https://github.com/artemmavrin/focal-loss.git

    cd focal-loss

    # Optional but recommended: create and activate a new environment first

    make dev

This will additionally install the requirements needed to run tests, check code

coverage, and produce documentation.

References

----------

.. [1] T. Lin, P. Goyal, R. Girshick, K. He and P. Dollár. Focal loss for dense

    object detection. IEEE Transactions on Pattern Analysis and Machine

    Intelligence, 2018. (`DOI `__)

    (`arXiv preprint `__)