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diff --git a/python-segmentation-models-pytorch.spec b/python-segmentation-models-pytorch.spec new file mode 100644 index 0000000..c68df5e --- /dev/null +++ b/python-segmentation-models-pytorch.spec @@ -0,0 +1,1611 @@ +%global _empty_manifest_terminate_build 0 +Name: python-segmentation-models-pytorch +Version: 0.3.2 +Release: 1 +Summary: Image segmentation models with pre-trained backbones. PyTorch. +License: MIT +URL: https://github.com/qubvel/segmentation_models.pytorch +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/97/68/119271a9693f516cfb1659023fed92a9048904af163243dd7a24a47d5115/segmentation_models_pytorch-0.3.2.tar.gz +BuildArch: noarch + +Requires: python3-torchvision +Requires: python3-pretrainedmodels +Requires: python3-efficientnet-pytorch +Requires: python3-timm +Requires: python3-tqdm +Requires: python3-pillow +Requires: python3-pytest +Requires: python3-mock +Requires: python3-pre-commit +Requires: python3-black +Requires: python3-flake8 +Requires: python3-flake8-docstrings + +%description + +<div align="center"> + + +**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/) +<br> +[](https://pypi.org/project/segmentation-models-pytorch/) +[](https://pepy.tech/project/segmentation-models-pytorch) +<br> +[](https://pepy.tech/project/segmentation-models-pytorch) +[](https://pepy.tech/project/segmentation-models-pytorch) + +</div> + +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) + +### ⏳ Quick start <a name="start"></a> + +#### 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 <a name="examples"></a> + - 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)). + +### 📦 Models <a name="models"></a> + +#### Architectures <a name="architectures"></a> + - 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)] + +#### Encoders <a name="encoders"></a> + +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). + +<details> +<summary style="margin-left: 25px;">ResNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|resnet18 |imagenet / ssl / swsl |11M | +|resnet34 |imagenet |21M | +|resnet50 |imagenet / ssl / swsl |23M | +|resnet101 |imagenet |42M | +|resnet152 |imagenet |58M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">ResNeXt</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">ResNeSt</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Res2Ne(X)t</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">RegNet(x/y)</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">GERNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|timm-gernet_s |imagenet |6M | +|timm-gernet_m |imagenet |18M | +|timm-gernet_l |imagenet |28M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">SE-Net</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">SK-ResNe(X)t</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|timm-skresnet18 |imagenet |11M | +|timm-skresnet34 |imagenet |21M | +|timm-skresnext50_32x4d |imagenet |25M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">DenseNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|densenet121 |imagenet |6M | +|densenet169 |imagenet |12M | +|densenet201 |imagenet |18M | +|densenet161 |imagenet |26M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Inception</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|inceptionresnetv2 |imagenet / imagenet+background |54M | +|inceptionv4 |imagenet / imagenet+background |41M | +|xception |imagenet |22M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">EfficientNet</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">MobileNet</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">DPN</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">VGG</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Mix Vision Transformer</summary> +<div style="margin-left: 25px;"> + +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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">MobileOne</summary> +<div style="margin-left: 25px;"> + +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 | + +</div> +</details> + + +\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)). + +#### Timm Encoders <a name="timm"></a> + +[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) + +### 🔁 Models API <a name="api"></a> + + - `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 <a name="installation"></a> +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 linting and formatting pre-commit hooks +```bash +pip install pre-commit black==22.3.0 flake8==4.0.1 +pre-commit install +``` + +##### Run tests +```bash +pytest -p no:cacheprovider +``` + +##### Run tests in docker +```bash +$ 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) +```bash +$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py +``` + +### 📝 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 <a name="license"></a> +Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE) + + + + +%package -n python3-segmentation-models-pytorch +Summary: Image segmentation models with pre-trained backbones. PyTorch. +Provides: python-segmentation-models-pytorch +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-segmentation-models-pytorch + +<div align="center"> + + +**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/) +<br> +[](https://pypi.org/project/segmentation-models-pytorch/) +[](https://pepy.tech/project/segmentation-models-pytorch) +<br> +[](https://pepy.tech/project/segmentation-models-pytorch) +[](https://pepy.tech/project/segmentation-models-pytorch) + +</div> + +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) + +### ⏳ Quick start <a name="start"></a> + +#### 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 <a name="examples"></a> + - 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)). + +### 📦 Models <a name="models"></a> + +#### Architectures <a name="architectures"></a> + - 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)] + +#### Encoders <a name="encoders"></a> + +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). + +<details> +<summary style="margin-left: 25px;">ResNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|resnet18 |imagenet / ssl / swsl |11M | +|resnet34 |imagenet |21M | +|resnet50 |imagenet / ssl / swsl |23M | +|resnet101 |imagenet |42M | +|resnet152 |imagenet |58M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">ResNeXt</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">ResNeSt</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Res2Ne(X)t</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">RegNet(x/y)</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">GERNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|timm-gernet_s |imagenet |6M | +|timm-gernet_m |imagenet |18M | +|timm-gernet_l |imagenet |28M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">SE-Net</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">SK-ResNe(X)t</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|timm-skresnet18 |imagenet |11M | +|timm-skresnet34 |imagenet |21M | +|timm-skresnext50_32x4d |imagenet |25M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">DenseNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|densenet121 |imagenet |6M | +|densenet169 |imagenet |12M | +|densenet201 |imagenet |18M | +|densenet161 |imagenet |26M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Inception</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|inceptionresnetv2 |imagenet / imagenet+background |54M | +|inceptionv4 |imagenet / imagenet+background |41M | +|xception |imagenet |22M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">EfficientNet</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">MobileNet</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">DPN</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">VGG</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Mix Vision Transformer</summary> +<div style="margin-left: 25px;"> + +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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">MobileOne</summary> +<div style="margin-left: 25px;"> + +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 | + +</div> +</details> + + +\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)). + +#### Timm Encoders <a name="timm"></a> + +[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) + +### 🔁 Models API <a name="api"></a> + + - `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 <a name="installation"></a> +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 linting and formatting pre-commit hooks +```bash +pip install pre-commit black==22.3.0 flake8==4.0.1 +pre-commit install +``` + +##### Run tests +```bash +pytest -p no:cacheprovider +``` + +##### Run tests in docker +```bash +$ 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) +```bash +$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py +``` + +### 📝 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 <a name="license"></a> +Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE) + + + + +%package help +Summary: Development documents and examples for segmentation-models-pytorch +Provides: python3-segmentation-models-pytorch-doc +%description help + +<div align="center"> + + +**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/) +<br> +[](https://pypi.org/project/segmentation-models-pytorch/) +[](https://pepy.tech/project/segmentation-models-pytorch) +<br> +[](https://pepy.tech/project/segmentation-models-pytorch) +[](https://pepy.tech/project/segmentation-models-pytorch) + +</div> + +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) + +### ⏳ Quick start <a name="start"></a> + +#### 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 <a name="examples"></a> + - 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)). + +### 📦 Models <a name="models"></a> + +#### Architectures <a name="architectures"></a> + - 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)] + +#### Encoders <a name="encoders"></a> + +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). + +<details> +<summary style="margin-left: 25px;">ResNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|resnet18 |imagenet / ssl / swsl |11M | +|resnet34 |imagenet |21M | +|resnet50 |imagenet / ssl / swsl |23M | +|resnet101 |imagenet |42M | +|resnet152 |imagenet |58M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">ResNeXt</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">ResNeSt</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Res2Ne(X)t</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">RegNet(x/y)</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">GERNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|timm-gernet_s |imagenet |6M | +|timm-gernet_m |imagenet |18M | +|timm-gernet_l |imagenet |28M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">SE-Net</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">SK-ResNe(X)t</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|timm-skresnet18 |imagenet |11M | +|timm-skresnet34 |imagenet |21M | +|timm-skresnext50_32x4d |imagenet |25M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">DenseNet</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|densenet121 |imagenet |6M | +|densenet169 |imagenet |12M | +|densenet201 |imagenet |18M | +|densenet161 |imagenet |26M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Inception</summary> +<div style="margin-left: 25px;"> + +|Encoder |Weights |Params, M | +|--------------------------------|:------------------------------:|:------------------------------:| +|inceptionresnetv2 |imagenet / imagenet+background |54M | +|inceptionv4 |imagenet / imagenet+background |41M | +|xception |imagenet |22M | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">EfficientNet</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">MobileNet</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">DPN</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">VGG</summary> +<div style="margin-left: 25px;"> + +|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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">Mix Vision Transformer</summary> +<div style="margin-left: 25px;"> + +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 | + +</div> +</details> + +<details> +<summary style="margin-left: 25px;">MobileOne</summary> +<div style="margin-left: 25px;"> + +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 | + +</div> +</details> + + +\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)). + +#### Timm Encoders <a name="timm"></a> + +[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) + +### 🔁 Models API <a name="api"></a> + + - `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 <a name="installation"></a> +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 linting and formatting pre-commit hooks +```bash +pip install pre-commit black==22.3.0 flake8==4.0.1 +pre-commit install +``` + +##### Run tests +```bash +pytest -p no:cacheprovider +``` + +##### Run tests in docker +```bash +$ 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) +```bash +$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py +``` + +### 📝 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 <a name="license"></a> +Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE) + + + + +%prep +%autosetup -n segmentation-models-pytorch-0.3.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-segmentation-models-pytorch -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.2-1 +- Package Spec generated |