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TensorFlow implementation of focal loss.

Project description

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TensorFlow implementation of focal loss [1]: a loss function generalizing binary cross-entropy loss that penalizes hard-to-classify examples.

The focal_loss package provides a function binary_focal_loss and a class BinaryFocalLoss that can be used as stand-in replacements for tf.keras.losses functions and classes, respectively.

# 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)
    metrics=...,
)
history = model.fit(...)
Focal loss plot

Installation

  1. Make sure that a CPU or GPU version of TensorFlow 2.0 or later is installed (see this link for installation instructions).

  2. The focal_loss package can be installed using the pip utility. For the latest version, install directly from the package’s GitHub page:

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

    Alternatively, install the a recent release from the Python Package Index (PyPI):

    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 make dev:

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

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