focal-loss

========== Focal Loss

.. image:: https://img.shields.io/pypi/pyversions/focal-loss :target: https://pypi.org/project/focal-loss :alt: Python Version

.. image:: https://img.shields.io/pypi/v/focal-loss :target: https://pypi.org/project/focal-loss :alt: PyPI Package Version

.. image:: https://img.shields.io/github/last-commit/artemmavrin/focal-loss/master :target: https://github.com/artemmavrin/focal-loss :alt: Last Commit

.. image:: https://github.com/artemmavrin/focal-loss/workflows/Python%20package/badge.svg :target: https://github.com/artemmavrin/focal-loss/actions?query=workflow%3A%22Python+package%22 :alt: Build Status

.. image:: https://codecov.io/gh/artemmavrin/focal-loss/branch/master/graph/badge.svg :target: https://codecov.io/gh/artemmavrin/focal-loss :alt: Code Coverage

.. image:: https://readthedocs.org/projects/focal-loss/badge/?version=latest :target: https://focal-loss.readthedocs.io/en/latest/ :alt: Documentation Status

.. image:: https://img.shields.io/github/license/artemmavrin/focal-loss :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 <https://focal-loss.readthedocs.io/en/latest/>__.

.. image:: docs/source/images/focal-loss.png :alt: Focal loss plot

Installation

The focal_loss package can be installed using the pip <https://pip.pypa.io/en/stable/>__ utility. For the latest version, install directly from the package's GitHub page <https://github.com/artemmavrin/focal-loss>__:

.. code-block:: bash

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

Alternatively, install a recent release from the Python Package Index (PyPI) <https://pypi.org/project/focal-loss>__:

.. 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 <https://doi.org/10.1109/TPAMI.2018.2858826>) (arXiv preprint <https://arxiv.org/abs/1708.02002>)