qubvel/segmentation_models

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<p align="center">
  <img src="https://i.ibb.co/GtxGS8m/Segmentation-Models-V1-Side-3-1.png">
  <b>Python library with Neural Networks for Image Segmentation based on <a href=https://www.keras.io>Keras</a> and <a href=https://www.tensorflow.org>TensorFlow</a>.
  </b>
  <br></br>

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The main features of this library are:

• High level API (just two lines of code to create model for segmentation)
• 4 models architectures for binary and multi-class image segmentation (including legendary Unet)
• 25 available backbones for each architecture
• All backbones have pre-trained weights for faster and better convergence
• Helpful segmentation losses (Jaccard, Dice, Focal) and metrics (IoU, F-score)

Important note

Some models of version ``1.*`` are not compatible with previously trained models,
if you have such models and want to load them - roll back with:

$ pip install -U segmentation-models==0.2.1

Table of Contents

 - `Quick start`_
 - `Simple training pipeline`_
 - `Examples`_
 - `Models and Backbones`_
 - `Installation`_
 - `Documentation`_
 - `Change log`_
 - `Citing`_
 - `License`_
 
Quick start
~~~~~~~~~~~
Library is build to work together with Keras and TensorFlow Keras frameworks

.. code:: python

    import segmentation_models as sm
    # Segmentation Models: using `keras` framework.

By default it tries to import ``keras``, if it is not installed, it will try to start with ``tensorflow.keras`` framework.
There are several ways to choose framework:

- Provide environment variable ``SM_FRAMEWORK=keras`` / ``SM_FRAMEWORK=tf.keras`` before import ``segmentation_models``
- Change framework ``sm.set_framework('keras')`` /  ``sm.set_framework('tf.keras')``

You can also specify what kind of ``image_data_format`` to use, segmentation-models works with both: ``channels_last`` and ``channels_first``.
This can be useful for further model conversion to Nvidia TensorRT format or optimizing model for cpu/gpu computations.

.. code:: python

    import keras
    # or from tensorflow import keras

    keras.backend.set_image_data_format('channels_last')
    # or keras.backend.set_image_data_format('channels_first')

Created segmentation model is just an instance of Keras Model, which can be build as easy as:

.. code:: python
    
    model = sm.Unet()
    
Depending on the task, you can change the network architecture by choosing backbones with fewer or more parameters and use pretrainded weights to initialize it:

.. code:: python

    model = sm.Unet('resnet34', encoder_weights='imagenet')

Change number of output classes in the model (choose your case):

.. code:: python
    
    # binary segmentation (this parameters are default when you call Unet('resnet34')
    model = sm.Unet('resnet34', classes=1, activation='sigmoid')
    
.. code:: python
    
    # multiclass segmentation with non overlapping class masks (your classes + background)
    model = sm.Unet('resnet34', classes=3, activation='softmax')
    
.. code:: python
    
    # multiclass segmentation with independent overlapping/non-overlapping class masks
    model = sm.Unet('resnet34', classes=3, activation='sigmoid')
    
    
Change input shape of the model:

.. code:: python
    
    # if you set input channels not equal to 3, you have to set encoder_weights=None
    # how to handle such case with encoder_weights='imagenet' described in docs
    model = Unet('resnet34', input_shape=(None, None, 6), encoder_weights=None)
   
Simple training pipeline

.. code:: python

import segmentation_models as sm

BACKBONE = 'resnet34'
preprocess_input = sm.get_preprocessing(BACKBONE)

# load your data
x_train, y_train, x_val, y_val = load_data(...)

# preprocess input
x_train = preprocess_input(x_train)
x_val = preprocess_input(x_val)

# define model
model = sm.Unet(BACKBONE, encoder_weights='imagenet')
model.compile(
    'Adam',
    loss=sm.losses.bce_jaccard_loss,
    metrics=[sm.metrics.iou_score],
)

# fit model
# if you use data generator use model.fit_generator(...) instead of model.fit(...)
# more about `fit_generator` here: https://keras.io/models/sequential/#fit_generator
model.fit(
   x=x_train,
   y=y_train,
   batch_size=16,
   epochs=100,
   validation_data=(x_val, y_val),
)

Same manipulations can be done with Linknet, PSPNet and FPN. For more detailed information about models API and use cases Read the Docs <https://segmentation-models.readthedocs.io/en/latest/>__.

Examples

Models training examples:
 - [Jupyter Notebook] Binary segmentation (`cars`) on CamVid dataset `here <https://github.com/qubvel/segmentation_models/blob/master/examples/binary%20segmentation%20(camvid).ipynb>`__.
 - [Jupyter Notebook] Multi-class segmentation (`cars`, `pedestrians`) on CamVid dataset `here <https://github.com/qubvel/segmentation_models/blob/master/examples/multiclass%20segmentation%20(camvid).ipynb>`__.

Models and Backbones

Models

• Unet <https://arxiv.org/abs/1505.04597>__
• FPN <http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf>__
• Linknet <https://arxiv.org/abs/1707.03718>__
• PSPNet <https://arxiv.org/abs/1612.01105>__

============= ============== Unet Linknet ============= ============== |unet_image| |linknet_image| ============= ==============

============= ============== PSPNet FPN ============= ============== |psp_image| |fpn_image| ============= ==============

.. _Unet: https://github.com/qubvel/segmentation_models/blob/readme/LICENSE .. _Linknet: https://arxiv.org/abs/1707.03718 .. _PSPNet: https://arxiv.org/abs/1612.01105 .. _FPN: http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf

.. |unet_image| image:: https://github.com/qubvel/segmentation_models/blob/master/images/unet.png .. |linknet_image| image:: https://github.com/qubvel/segmentation_models/blob/master/images/linknet.png .. |psp_image| image:: https://github.com/qubvel/segmentation_models/blob/master/images/pspnet.png .. |fpn_image| image:: https://github.com/qubvel/segmentation_models/blob/master/images/fpn.png

Backbones

.. table::

=============  ===== 
Type           Names
=============  =====
VGG            ``'vgg16' 'vgg19'``
ResNet         ``'resnet18' 'resnet34' 'resnet50' 'resnet101' 'resnet152'``
SE-ResNet      ``'seresnet18' 'seresnet34' 'seresnet50' 'seresnet101' 'seresnet152'``
ResNeXt        ``'resnext50' 'resnext101'``
SE-ResNeXt     ``'seresnext50' 'seresnext101'``
SENet154       ``'senet154'``
DenseNet       ``'densenet121' 'densenet169' 'densenet201'`` 
Inception      ``'inceptionv3' 'inceptionresnetv2'``
MobileNet      ``'mobilenet' 'mobilenetv2'``
EfficientNet   ``'efficientnetb0' 'efficientnetb1' 'efficientnetb2' 'efficientnetb3' 'efficientnetb4' 'efficientnetb5' efficientnetb6' efficientnetb7'``
=============  =====

.. epigraph:: All backbones have weights trained on 2012 ILSVRC ImageNet dataset (encoder_weights='imagenet').

Installation

**Requirements**

1) python 3
2) keras >= 2.2.0 or tensorflow >= 1.13
3) keras-applications >= 1.0.7, <=1.0.8
4) image-classifiers == 1.0.*
5) efficientnet == 1.0.*

**PyPI stable package**

.. code:: bash

    $ pip install -U segmentation-models

**PyPI latest package**

.. code:: bash

    $ pip install -U --pre segmentation-models

**Source latest version**

.. code:: bash

    $ pip install git+https://github.com/qubvel/segmentation_models
    
Documentation

Latest documentation is avaliable on Read the Docs <https://segmentation-models.readthedocs.io/en/latest/>__

Change Log

To see important changes between versions look at CHANGELOG.md_

Citing
~~~~~~~~

.. code::

    @misc{Yakubovskiy:2019,
      Author = {Pavel Iakubovskii},
      Title = {Segmentation Models},
      Year = {2019},
      Publisher = {GitHub},
      Journal = {GitHub repository},
      Howpublished = {\url{https://github.com/qubvel/segmentation_models}}
    } 

License
~~~~~~~
Project is distributed under `MIT Licence`_.

.. _CHANGELOG.md: https://github.com/qubvel/segmentation_models/blob/master/CHANGELOG.md
.. _`MIT Licence`: https://github.com/qubvel/segmentation_models/blob/master/LICENSE