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https://github.com/qubvel/segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
https://github.com/qubvel/segmentation_models.pytorch
deeplab-v3-plus deeplabv3 fpn hacktoberfest image-processing image-segmentation imagenet linknet models pretrained-backbones pretrained-models pretrained-weights pspnet pytorch segmentation segmentation-models semantic-segmentation unet unet-pytorch unetplusplus
Last synced: 2 months ago
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Segmentation models with pretrained backbones. PyTorch.
- Host: GitHub
- URL: https://github.com/qubvel/segmentation_models.pytorch
- Owner: qubvel
- License: mit
- Created: 2019-03-01T16:21:21.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-11-27T01:24:07.000Z (6 months ago)
- Last Synced: 2024-01-06T04:49:54.504Z (5 months ago)
- Topics: deeplab-v3-plus, deeplabv3, fpn, hacktoberfest, image-processing, image-segmentation, imagenet, linknet, models, pretrained-backbones, pretrained-models, pretrained-weights, pspnet, pytorch, segmentation, segmentation-models, semantic-segmentation, unet, unet-pytorch, unetplusplus
- Language: Python
- Homepage:
- Size: 5.95 MB
- Stars: 8,391
- Watchers: 75
- Forks: 1,556
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Lists
- awesome-stars - qubvel/segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch. (Python)
- awesome-made-by-russians - Segmentation Models (PyTorch)
- awesome-machine-learning - segmentation_models.pytorch - A PyTorch-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in images. (Python / General-Purpose Machine Learning)
- my-awesome-stars - qubvel/segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch. (Python)
- awesome-machine-learning - segmentation_models.pytorch - A PyTorch-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in images. (Python / General-Purpose Machine Learning)
- awesome-machine-learning-colab-ipynb - PyTorch Segmentation models
- awesome-python-data-science - segmentation_models.pytorch - Segmentation models with pretrained backbones. (Deep Learning Tools)
- awesome-cv - segmentation_models.pytorch
README
**Python library with Neural Networks for Image
Segmentation based on [PyTorch](https://pytorch.org/).**
[](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE)
[](https://github.com/qubvel/segmentation_models.pytorch/actions/workflows/tests.yml)
[](https://smp.readthedocs.io/en/latest/)
[](https://pypi.org/project/segmentation-models-pytorch/)
[](https://pepy.tech/project/segmentation-models-pytorch)
[](https://pepy.tech/project/segmentation-models-pytorch)
[](https://pepy.tech/project/segmentation-models-pytorch)
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)
- 124 available encoders (and 500+ encoders from [timm](https://github.com/rwightman/pytorch-image-models))
- All encoders have pre-trained weights for faster and better convergence
- Popular metrics and losses for training routines
### [📚 Project Documentation 📚](http://smp.readthedocs.io/)
Visit [Read The Docs Project Page](https://smp.readthedocs.io/) or read following README to know more about Segmentation Models Pytorch (SMP for short) library
### 📋 Table of content
1. [Quick start](#start)
2. [Examples](#examples)
3. [Models](#models)
1. [Architectures](#architectures)
2. [Encoders](#encoders)
3. [Timm Encoders](#timm)
4. [Models API](#api)
1. [Input channels](#input-channels)
2. [Auxiliary classification output](#auxiliary-classification-output)
3. [Depth](#depth)
5. [Installation](#installation)
6. [Competitions won with the library](#competitions-won-with-the-library)
7. [Contributing](#contributing)
8. [Citing](#citing)
9. [License](#license)
#### 1. Create your first Segmentation model with SMP
Segmentation model is just a PyTorch nn.Module, which can be created as easy as:
```python
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](#architectures) with available model architectures
- see [table](#encoders) 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 you better results (higher metric score and faster convergence). It is **not necessary** in case you train the whole model, not only decoder.
```python
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](https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb) and [](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb)
- Training model for cars segmentation on CamVid dataset [here](https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/cars%20segmentation%20(camvid).ipynb).
- Training SMP model with [Catalyst](https://github.com/catalyst-team/catalyst) (high-level framework for PyTorch), [TTAch](https://github.com/qubvel/ttach) (TTA library for PyTorch) and [Albumentations](https://github.com/albu/albumentations) (fast image augmentation library) - [here](https://github.com/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb) [](https://colab.research.google.com/github/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb)
- Training SMP model with [Pytorch-Lightning](https://pytorch-lightning.readthedocs.io) framework - [here](https://github.com/ternaus/cloths_segmentation) (clothes binary segmentation by [@ternaus](https://github.com/ternaus)).
#### Architectures
- Unet [[paper](https://arxiv.org/abs/1505.04597)] [[docs](https://smp.readthedocs.io/en/latest/models.html#unet)]
- Unet++ [[paper](https://arxiv.org/pdf/1807.10165.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id2)]
- MAnet [[paper](https://ieeexplore.ieee.org/abstract/document/9201310)] [[docs](https://smp.readthedocs.io/en/latest/models.html#manet)]
- Linknet [[paper](https://arxiv.org/abs/1707.03718)] [[docs](https://smp.readthedocs.io/en/latest/models.html#linknet)]
- FPN [[paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#fpn)]
- PSPNet [[paper](https://arxiv.org/abs/1612.01105)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pspnet)]
- PAN [[paper](https://arxiv.org/abs/1805.10180)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pan)]
- DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)] [[docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3)]
- DeepLabV3+ [[paper](https://arxiv.org/abs/1802.02611)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id9)]
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 |
Mix Vision Transformer
Backbone from SegFormer pretrained on Imagenet! Can be used with other decoders from package, you can combine Mix Vision Transformer with Unet, FPN and others!
Limitations:
- encoder is **not** supported by Linknet, Unet++
- encoder is supported by FPN only for encoder **depth = 5**
|Encoder |Weights |Params, M |
|--------------------------------|:------------------------------:|:------------------------------:|
|mit_b0 |imagenet |3M |
|mit_b1 |imagenet |13M |
|mit_b2 |imagenet |24M |
|mit_b3 |imagenet |44M |
|mit_b4 |imagenet |60M |
|mit_b5 |imagenet |81M |
MobileOne
Apple's "sub-one-ms" Backbone pretrained on Imagenet! Can be used with all decoders.
Note: In the official github repo the s0 variant has additional num_conv_branches, leading to more params than s1.
|Encoder |Weights |Params, M |
|--------------------------------|:------------------------------:|:------------------------------:|
|mobileone_s0 |imagenet |4.6M |
|mobileone_s1 |imagenet |4.0M |
|mobileone_s2 |imagenet |6.5M |
|mobileone_s3 |imagenet |8.8M |
|mobileone_s4 |imagenet |13.6M |
\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)).
[docs](https://smp.readthedocs.io/en/latest/encoders_timm.html)
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
- not all transformer models have ``features_only`` functionality implemented that is required for encoder
- some models have inappropriate strides
Total number of supported encoders: 549
- [table with available encoders](https://smp.readthedocs.io/en/latest/encoders_timm.html)
- `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`.
```python
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:
```python
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`.
```python
model = smp.Unet('resnet34', encoder_depth=4)
```
### 🛠 Installation
PyPI version:
```bash
$ pip install segmentation-models-pytorch
````
Latest version from source:
```bash
$ 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](https://github.com/qubvel/segmentation_models.pytorch/blob/master/HALLOFFAME.md) you can find competitions, names of the winners and links to their solutions.
### 🤝 Contributing
#### Install SMP
```bash
make install_dev # create .venv, install SMP in dev mode
```
#### Run tests and code checks
```bash
make all # run flake8, black, tests
```
#### Update table with encoders
```bash
make table # generate table with encoders and print to stdout
```
### 📝 Citing
```
@misc{Iakubovskii:2019,
Author = {Pavel Iakubovskii},
Title = {Segmentation Models Pytorch},
Year = {2019},
Publisher = {GitHub},
Journal = {GitHub repository},
Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}}
}
```
### 🛡️ License
Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE)