The main features of this library are:

• High level API (just two lines to create a neural network)
• 9 models architectures for binary and multi class segmentation (including legendary Unet)
• 113 available encoders (and 400+ encoders from timm)
• All encoders have pre-trained weights for faster and better convergence
• Popular metrics and losses for training routines

📚 Project Documentation 📚

Visit Read The Docs Project Page or read following README to know more about Segmentation Models Pytorch (SMP for short) library

📋 Table of content

1. Quick start
2. Examples
3. Models
   1. Architectures
   2. Encoders
   3. Timm Encoders
4. Models API
   1. Input channels
   2. Auxiliary classification output
   3. Depth
5. Installation
6. Competitions won with the library
7. Contributing
8. Citing
9. License

⏳ Quick start

1. Create your first Segmentation model with SMP

Segmentation model is just a PyTorch nn.Module, which can be created as easy as:

import segmentation_models_pytorch as smp

model = smp.Unet(
    encoder_name="resnet34",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
    encoder_weights="imagenet",     # use `imagenet` pre-trained weights for encoder initialization
    in_channels=1,                  # model input channels (1 for gray-scale images, 3 for RGB, etc.)
    classes=3,                      # model output channels (number of classes in your dataset)
)

• see table with available model architectures
• see table with available encoders and their corresponding weights

2. Configure data preprocessing

All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give your better results (higher metric score and faster convergence). It is not necessary in case you train the whole model, not only decoder.

from segmentation_models_pytorch.encoders import get_preprocessing_fn

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

Congratulations! You are done! Now you can train your model with your favorite framework!

💡 Examples

• Training model for pets binary segmentation with Pytorch-Lightning notebook and
• Training model for cars segmentation on CamVid dataset here.
• Training SMP model with Catalyst (high-level framework for PyTorch), TTAch (TTA library for PyTorch) and Albumentations (fast image augmentation library) - here
• Training SMP model with Pytorch-Lightning framework - here (clothes binary segmentation by @ternaus).

📦 Models

Architectures

• Unet [paper] [docs]
• Unet++ [paper] [docs]
• MAnet [paper] [docs]
• Linknet [paper] [docs]
• FPN [paper] [docs]
• PSPNet [paper] [docs]
• PAN [paper] [docs]
• DeepLabV3 [paper] [docs]
• DeepLabV3+ [paper] [docs]

Encoders

The following is a list of supported encoders in the SMP. Select the appropriate family of encoders and click to expand the table and select a specific encoder and its pre-trained weights (encoder_name and encoder_weights parameters).

ResNet

Encoder	Weights	Params, M
resnet18	imagenet / ssl / swsl	11M
resnet34	imagenet	21M
resnet50	imagenet / ssl / swsl	23M
resnet101	imagenet	42M
resnet152	imagenet	58M

ResNeXt

Encoder	Weights	Params, M
resnext50_32x4d	imagenet / ssl / swsl	22M
resnext101_32x4d	ssl / swsl	42M
resnext101_32x8d	imagenet / instagram / ssl / swsl	86M
resnext101_32x16d	instagram / ssl / swsl	191M
resnext101_32x32d	instagram	466M
resnext101_32x48d	instagram	826M

ResNeSt

Encoder	Weights	Params, M
timm-resnest14d	imagenet	8M
timm-resnest26d	imagenet	15M
timm-resnest50d	imagenet	25M
timm-resnest101e	imagenet	46M
timm-resnest200e	imagenet	68M
timm-resnest269e	imagenet	108M
timm-resnest50d_4s2x40d	imagenet	28M
timm-resnest50d_1s4x24d	imagenet	23M

Res2Ne(X)t

Encoder	Weights	Params, M
timm-res2net50_26w_4s	imagenet	23M
timm-res2net101_26w_4s	imagenet	43M
timm-res2net50_26w_6s	imagenet	35M
timm-res2net50_26w_8s	imagenet	46M
timm-res2net50_48w_2s	imagenet	23M
timm-res2net50_14w_8s	imagenet	23M
timm-res2next50	imagenet	22M

RegNet(x/y)

Encoder	Weights	Params, M
timm-regnetx_002	imagenet	2M
timm-regnetx_004	imagenet	4M
timm-regnetx_006	imagenet	5M
timm-regnetx_008	imagenet	6M
timm-regnetx_016	imagenet	8M
timm-regnetx_032	imagenet	14M
timm-regnetx_040	imagenet	20M
timm-regnetx_064	imagenet	24M
timm-regnetx_080	imagenet	37M
timm-regnetx_120	imagenet	43M
timm-regnetx_160	imagenet	52M
timm-regnetx_320	imagenet	105M
timm-regnety_002	imagenet	2M
timm-regnety_004	imagenet	3M
timm-regnety_006	imagenet	5M
timm-regnety_008	imagenet	5M
timm-regnety_016	imagenet	10M
timm-regnety_032	imagenet	17M
timm-regnety_040	imagenet	19M
timm-regnety_064	imagenet	29M
timm-regnety_080	imagenet	37M
timm-regnety_120	imagenet	49M
timm-regnety_160	imagenet	80M
timm-regnety_320	imagenet	141M

GERNet

Encoder	Weights	Params, M
timm-gernet_s	imagenet	6M
timm-gernet_m	imagenet	18M
timm-gernet_l	imagenet	28M

SE-Net

Encoder	Weights	Params, M
senet154	imagenet	113M
se_resnet50	imagenet	26M
se_resnet101	imagenet	47M
se_resnet152	imagenet	64M
se_resnext50_32x4d	imagenet	25M
se_resnext101_32x4d	imagenet	46M

SK-ResNe(X)t

Encoder	Weights	Params, M
timm-skresnet18	imagenet	11M
timm-skresnet34	imagenet	21M
timm-skresnext50_32x4d	imagenet	25M

DenseNet

Encoder	Weights	Params, M
densenet121	imagenet	6M
densenet169	imagenet	12M
densenet201	imagenet	18M
densenet161	imagenet	26M

Inception

Encoder	Weights	Params, M
inceptionresnetv2	imagenet / imagenet+background	54M
inceptionv4	imagenet / imagenet+background	41M
xception	imagenet	22M

EfficientNet

Encoder	Weights	Params, M
efficientnet-b0	imagenet	4M
efficientnet-b1	imagenet	6M
efficientnet-b2	imagenet	7M
efficientnet-b3	imagenet	10M
efficientnet-b4	imagenet	17M
efficientnet-b5	imagenet	28M
efficientnet-b6	imagenet	40M
efficientnet-b7	imagenet	63M
timm-efficientnet-b0	imagenet / advprop / noisy-student	4M
timm-efficientnet-b1	imagenet / advprop / noisy-student	6M
timm-efficientnet-b2	imagenet / advprop / noisy-student	7M
timm-efficientnet-b3	imagenet / advprop / noisy-student	10M
timm-efficientnet-b4	imagenet / advprop / noisy-student	17M
timm-efficientnet-b5	imagenet / advprop / noisy-student	28M
timm-efficientnet-b6	imagenet / advprop / noisy-student	40M
timm-efficientnet-b7	imagenet / advprop / noisy-student	63M
timm-efficientnet-b8	imagenet / advprop	84M
timm-efficientnet-l2	noisy-student	474M
timm-efficientnet-lite0	imagenet	4M
timm-efficientnet-lite1	imagenet	5M
timm-efficientnet-lite2	imagenet	6M
timm-efficientnet-lite3	imagenet	8M
timm-efficientnet-lite4	imagenet	13M

MobileNet

Encoder	Weights	Params, M
mobilenet_v2	imagenet	2M
timm-mobilenetv3_large_075	imagenet	1.78M
timm-mobilenetv3_large_100	imagenet	2.97M
timm-mobilenetv3_large_minimal_100	imagenet	1.41M
timm-mobilenetv3_small_075	imagenet	0.57M
timm-mobilenetv3_small_100	imagenet	0.93M
timm-mobilenetv3_small_minimal_100	imagenet	0.43M

DPN

Encoder	Weights	Params, M
dpn68	imagenet	11M
dpn68b	imagenet+5k	11M
dpn92	imagenet+5k	34M
dpn98	imagenet	58M
dpn107	imagenet+5k	84M
dpn131	imagenet	76M

VGG

Encoder	Weights	Params, M
vgg11	imagenet	9M
vgg11_bn	imagenet	9M
vgg13	imagenet	9M
vgg13_bn	imagenet	9M
vgg16	imagenet	14M
vgg16_bn	imagenet	14M
vgg19	imagenet	20M
vgg19_bn	imagenet	20M

* ssl, swsl - semi-supervised and weakly-supervised learning on ImageNet (repo).

Timm Encoders

docs

Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported

• transformer models do not have features_only functionality implemented
• some models do not have appropriate strides

Total number of supported encoders: 467

• table with available encoders

🔁 Models API

• model.encoder - pretrained backbone to extract features of different spatial resolution
• model.decoder - depends on models architecture (Unet/Linknet/PSPNet/FPN)
• model.segmentation_head - last block to produce required number of mask channels (include also optional upsampling and activation)
• model.classification_head - optional block which create classification head on top of encoder
• model.forward(x) - sequentially pass x through model`s encoder, decoder and segmentation head (and classification head if specified)

Input channels

Input channels parameter allows you to create models, which process tensors with arbitrary number of channels. If you use pretrained weights from imagenet - weights of first convolution will be reused. For 1-channel case it would be a sum of weights of first convolution layer, otherwise channels would be populated with weights like new_weight[:, i] = pretrained_weight[:, i % 3] and than scaled with new_weight * 3 / new_in_channels.

model = smp.FPN('resnet34', in_channels=1)
mask = model(torch.ones([1, 1, 64, 64]))

Auxiliary classification output

All models support aux_params parameters, which is default set to None. If aux_params = None then classification auxiliary output is not created, else model produce not only mask, but also label output with shape NC. Classification head consists of GlobalPooling->Dropout(optional)->Linear->Activation(optional) layers, which can be configured by aux_params as follows:

aux_params=dict(
    pooling='avg',             # one of 'avg', 'max'
    dropout=0.5,               # dropout ratio, default is None
    activation='sigmoid',      # activation function, default is None
    classes=4,                 # define number of output labels
)
model = smp.Unet('resnet34', classes=4, aux_params=aux_params)
mask, label = model(x)

Depth

Depth parameter specify a number of downsampling operations in encoder, so you can make your model lighter if specify smaller depth.

model = smp.Unet('resnet34', encoder_depth=4)

🛠 Installation

PyPI version:

$ pip install segmentation-models-pytorch

Latest version from source:

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

🏆 Competitions won with the library

Segmentation Models package is widely used in the image segmentation competitions. Here you can find competitions, names of the winners and links to their solutions.

🤝 Contributing

Install linting and formatting pre-commit hooks

pip install pre-commit black flake8
pre-commit install

Run tests

pytest -p no:cacheprovider

Run tests in docker

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

Generate table with encoders (in case you add a new encoder)

$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py

📝 Citing

@misc{Yakubovskiy:2019,
  Author = {Pavel Yakubovskiy},
  Title = {Segmentation Models Pytorch},
  Year = {2020},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}}
}

🛡️ License

Project is distributed under MIT License