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d3m-segmentation-models-pytorch-0.1.3
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مشاهده بیشترتوضیحات
Image segmentation models with pre-trained backbones. PyTorch.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | d3m-segmentation-models-pytorch-0.1.3 |
| نام | d3m-segmentation-models-pytorch |
| نسخه کتابخانه | 0.1.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Pavel Yakubovskiy |
| ایمیل نویسنده | qubvel@gmail.com |
| آدرس صفحه اصلی | https://github.com/qubvel/segmentation_models.pytorch |
| آدرس اینترنتی | https://pypi.org/project/d3m-segmentation-models-pytorch/ |
| مجوز | MIT |
<div align="center">
**Python library with Neural Networks for Image
Segmentation based on [PyTorch](https://pytorch.org/).**
 [](https://travis-ci.com/qubvel/segmentation_models.pytorch) [](https://smp.readthedocs.io/en/latest/?badge=latest) <br>[](https://pepy.tech/project/segmentation-models-pytorch) [](https://shields.io/)
</div>
The main features of this library are:
- High level API (just two lines to create neural network)
- 8 models architectures for binary and multi class segmentation (including legendary Unet)
- 99 available encoders
- All encoders have pre-trained weights for faster and better convergence
### [📚 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](#architectires)
2. [Encoders](#encoders)
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` pretreined weights for encoder initialization
in_channels=1, # model input channels (1 for grayscale images, 3 for RGB, etc.)
classes=3, # model output channels (number of classes in your dataset)
)
```
- see [table](#architectires) with available model architectures
- see [table](#encoders) with avaliable encoders and its corresponding weights
#### 2. Configure data preprocessing
All encoders have pretrained weights. Preparing your data the same way as during weights pretraining may give your better results (higher metric score and faster convergence). But it is relevant only for 1-2-3-channels images and **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 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/master/examples/notebooks/segmentation-tutorial.ipynb) [](https://colab.research.google.com/github/catalyst-team/catalyst/blob/master/examples/notebooks/segmentation-tutorial.ipynb)
### 📦 Models <a name="models"></a>
#### Architectures <a name="architectires"></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)]
- 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#id8)]
#### 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;">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 |
</div>
</details>
<details>
<summary style="margin-left: 25px;">MobileNet</summary>
<div style="margin-left: 25px;">
|Encoder |Weights |Params, M |
|--------------------------------|:------------------------------:|:------------------------------:|
|mobilenet_v2 |imagenet |2M |
</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>
\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)).
### 🔁 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 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.
```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` 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:
```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 lighted 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
##### Run test
```bash
$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev pytest -p no:cacheprovider
```
##### Generate table
```bash
$ 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 <a name="license"></a>
Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE)
نیازمندی
| مقدار | نام |
|---|---|
| >=0.3.0 | torchvision |
| ==0.7.4 | pretrainedmodels |
| >=0.6.3 | efficientnet-pytorch |
| ==0.3.2 | timm |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.0.0 | Python |
نحوه نصب
نصب پکیج whl d3m-segmentation-models-pytorch-0.1.3:
pip install d3m-segmentation-models-pytorch-0.1.3.whl
نصب پکیج tar.gz d3m-segmentation-models-pytorch-0.1.3:
pip install d3m-segmentation-models-pytorch-0.1.3.tar.gz