keras_retinanet package

Subpackages

Submodules

keras_retinanet.initializers module

Copyright 2017-2018 Fizyr (https://fizyr.com)

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

class keras_retinanet.initializers.PriorProbability(probability=0.01)[source]

Bases: sphinx.ext.autodoc.importer._MockObject

Apply a prior probability to the weights.

get_config()[source]

keras_retinanet.losses module

Copyright 2017-2018 Fizyr (https://fizyr.com)

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

keras_retinanet.losses.focal(alpha=0.25, gamma=2.0)[source]

Create a functor for computing the focal loss.

Args
alpha: Scale the focal weight with alpha. gamma: Take the power of the focal weight with gamma.
Returns
A functor that computes the focal loss using the alpha and gamma.
keras_retinanet.losses.smooth_l1(sigma=3.0)[source]

Create a smooth L1 loss functor.

Args
sigma: This argument defines the point where the loss changes from L2 to L1.
Returns
A functor for computing the smooth L1 loss given target data and predicted data.

Module contents