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)
- 5 models architectures for binary and multi class segmentation (including legendary Unet)
- 46 available encoders for each architecture
- All encoders have pre-trained weights for faster and better convergence
- Quick start
- Examples
- Models
- Models API
- Installation
- Competitions won with the library
- Contributing
- Citing
- License
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')- 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
| Encoder | Weights | Params, M |
|---|---|---|
| resnet18 | imagenet | 11M |
| resnet34 | imagenet | 21M |
| resnet50 | imagenet | 23M |
| resnet101 | imagenet | 42M |
| resnet152 | imagenet | 58M |
| resnext50_32x4d | imagenet | 22M |
| resnext101_32x8d | imagenet |
86M |
| resnext101_32x16d | 191M | |
| resnext101_32x32d | 466M | |
| resnext101_32x48d | 826M | |
| dpn68 | imagenet | 11M |
| dpn68b | imagenet+5k | 11M |
| dpn92 | imagenet+5k | 34M |
| dpn98 | imagenet | 58M |
| dpn107 | imagenet+5k | 84M |
| dpn131 | imagenet | 76M |
| 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 |
| 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 |
| densenet121 | imagenet | 6M |
| densenet169 | imagenet | 12M |
| densenet201 | imagenet | 18M |
| densenet161 | imagenet | 26M |
| inceptionresnetv2 | imagenet imagenet+background |
54M |
| inceptionv4 | imagenet imagenet+background |
41M |
| 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 |
| mobilenet_v2 | imagenet | 2M |
| xception | imagenet | 22M |
model.encoder- pretrained backbone to extract features of different spatial resolutionmodel.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 encodermodel.forward(x)- sequentially passxthrough model`s encoder, decoder and segmentation head (and classification head if specified)
Input channels parameter allow 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- or 2- channels inputs, for input channels > 4 weights of first convolution will be initialized randomly.
model = smp.FPN('resnet34', in_channels=1)
mask = model(torch.ones([1, 1, 64, 64]))All models support aux_params parameters, which is default set to None.
If aux_params = None than classification auxiliary output is not created, else
model produce not only mask, but also label output with shape NC.
Classification head consist 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 parameter specify a number of downsampling operations in encoder, so you can make
your model lighted if specify smaller depth.
model = smp.Unet('resnet34', encoder_depth=4)PyPI version:
$ pip install segmentation-models-pytorchLatest version from source:
$ pip install git+https://github.com/qubvel/segmentation_models.pytorchSegmentation Models package is widely used in the image segmentation competitions.
Here you can find competitions, names of the winners and links to their solutions.
$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev pytest -p no:cacheprovider$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py@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}}
}
Project is distributed under MIT License