segmentation-models-pytorch 0.0.3

Image segmentation models with pre-trained backbones. PyTorch.

Segmentation models

Segmentation models is python library with Neural Networks for Image Segmentation based on PyTorch.

The main features of this library are:

• High level API (just two lines to create neural network)
• 4 models architectures for binary and multi class segmentation (including legendary Unet)
• 30 available encoders for each architecture
• All encoders have pre-trained weights for faster and better convergence

Table of content

1. Quick start
2. Examples
3. Models
   1. Architectures
   2. Encoders
   3. Pretrained weights
4. Models API
5. Installation
6. License

Quick start

Since the library is built on the PyTorch framework, created segmentation model is just a PyTorch nn.Module, which can be created as easy as:

import segmentation_models_pytorch as smp

model = smp.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:

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

Change number of output classes in the model:

model = smp.Unet('resnet34', classes=3, activation='softmax')

All models have pretrained encoders, so you have to prepare your data the same way as during weights pretraining:

from segmentation_models_pytorch.encoders import get_preprocessing_fn

preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')

Examples

• Training model for cars segmentation on CamVid dataset here.
• Training model with Catalyst (high-level framework for PyTorch) - here.

Models

Architectures

• Unet
• Linknet
• FPN
• PSPNet

Encoders

Type	Encoder names
VGG	vgg11, vgg13, vgg16, vgg19, vgg11bn, vgg13bn, vgg16bn, vgg19bn
DenseNet	densenet121, densenet169, densenet201, densenet161
DPN	dpn68, dpn68b, dpn92, dpn98, dpn107, dpn131
Inception	inceptionresnetv2
ResNet	resnet18, resnet34, resnet50, resnet101, resnet152
ResNeXt	resnext50_32x4d, resnext101_32x8d, resnext101_32x16d, resnext101_32x32d, resnext101_32x48d
SE-ResNet	se_resnet50, se_resnet101, se_resnet152
SE-ResNeXt	se_resnext50_32x4d, se_resnext101_32x4d
SENet	senet154

Weights

Weights name	Encoder names
imagenet+5k	dpn68b, dpn92, dpn107
imagenet	vgg11, vgg13, vgg16, vgg19, vgg11bn, vgg13bn, vgg16bn, vgg19bn, densenet121, densenet169, densenet201, densenet161, dpn68, dpn98, dpn131, inceptionresnetv2, resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d, resnext101_32x8d, se_resnet50, se_resnet101, se_resnet152, se_resnext50_32x4d, se_resnext101_32x4d, senet154
instagram	resnext101_32x8d, resnext101_32x16d, resnext101_32x32d, resnext101_32x48d

Models API

• model.encoder - pretrained backbone to extract features of different spatial resolution
• model.decoder - segmentation head, depends on models architecture (Unet/Linknet/PSPNet/FPN)
• model.activation - output activation function, one of sigmoid, softmax
• model.forward(x) - sequentially pass x through model`s encoder and decoder (return logits!)
• model.predict(x) - inference method, switch model to .eval() mode, call .forward(x) and apply activation function with torch.no_grad()

Installation

PyPI version:

$ pip install segmentation-models-pytorch

Latest version from source:

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

License

Project is distributed under MIT License

Run tests

$ docker build -f docker/Dockerfile.dev -t smp:dev .
$ docker run --rm smp:dev pytest -p no:cacheprovider