Metadata-Version: 2.1
Name: change-detection-pytorch
Version: 0.1.0
Summary: Change detection models with pre-trained backbones. Inspired by segmentation_models.pytorch.
Home-page: https://github.com/likyoo/change_detection.pytorch
Author: Kaiyu Li, Fulin Sun, Xudong Liu
Author-email: likyoo@sdust.edu.cn,linoemail@163.com
License: MIT
Description: 
        <h1 align="center">
          <b>Change Detection Models</b><br>
        </h1>
        <p align="center">
              <b>Python library with Neural Networks for Change Detection based on PyTorch.</b>
        </p>
        
        
        <img src="resources/model architecture.png" alt="model architecture" style="zoom:80%;" />
        
        
        This project is inspired by **[segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch)** and built based on it. 😄
        
        ### 🌱 How to use <a name="use"></a>
        
        Please refer to local_test.py temporarily.
        
        
        
        ### 🔭 Models <a name="models"></a>
        
        #### Architectures <a name="architectures"></a>
        - [x] Unet [[paper](https://arxiv.org/abs/1505.04597)]
        
        - [x] Unet++ [[paper](https://arxiv.org/pdf/1807.10165.pdf)]
        
        - [x] MAnet [[paper](https://ieeexplore.ieee.org/abstract/document/9201310)]
        
        - [x] Linknet [[paper](https://arxiv.org/abs/1707.03718)]
        
        - [x] FPN [[paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)]
        
        - [x] PSPNet [[paper](https://arxiv.org/abs/1612.01105)]
        
        - [x] PAN [[paper](https://arxiv.org/abs/1805.10180)]
        
        - [x] DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)]
        
        - [x] DeepLabV3+ [[paper](https://arxiv.org/abs/1802.02611)]
        
        - [x] UPerNet [[paper](https://arxiv.org/abs/1807.10221)]
        
        - [x] STANet [[paper](https://www.mdpi.com/2072-4292/12/10/1662)]
        
        #### Encoders <a name="encoders"></a>
        
        The following is a list of supported encoders in the CDP. 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>
        
        
        
        ### :truck: Dataset <a name="dataset"></a>
        
        - [x] [LEVIR-CD](https://justchenhao.github.io/LEVIR/)
        - [x] [SVCD](https://www.researchgate.net/publication/325470033_CHANGE_DETECTION_IN_REMOTE_SENSING_IMAGES_USING_CONDITIONAL_ADVERSARIAL_NETWORKS) [[google drive](https://drive.google.com/file/d/1GX656JqqOyBi_Ef0w65kDGVto-nHrNs9/edit) | [baidu disk](https://pan.baidu.com/s/1bU9bSRxQnlfw7OkOw7hqjA) (x8gi)] 
        - [ ] ...
        
        
        
        ### 🏆 Competitions won with the library
        
        `change_detection.pytorch` has competitiveness and potential in the change detection competitions.
        [Here](https://github.com/likyoo/change_detection.pytorch/blob/main/COMPETITIONS.md) you can find competitions, names of the winners and links to their solutions.
        
        
        
        ### :page_with_curl: Citing <a name="citing"></a>
        
        ```
        @misc{likyoocdp:2021,
          Author = {Kaiyu Li, Fulin Sun, Xudong Liu},
          Title = {Change Detection Pytorch},
          Year = {2021},
          Publisher = {GitHub},
          Journal = {GitHub repository},
          Howpublished = {\url{https://github.com/likyoo/change_detection.pytorch}}
        }
        ```
        
        
        
        ### :books: Reference <a name="reference"></a>
        
        - [qubvel/segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch)
        - [albumentations-team/albumentations](https://github.com/albumentations-team/albumentations)
        - [open-mmlab/mmsegmentation](https://github.com/open-mmlab/mmsegmentation)
        - [wenhwu/awesome-remote-sensing-change-detection](https://github.com/wenhwu/awesome-remote-sensing-change-detection)
        
        
        
        ### :mailbox: Contact<a name="contact"></a>
        
        ⚡⚡⚡ I am trying to build this project, if you are interested, don't hesitate to join us! 
        
        👯👯👯 Contact me at likyoo@sdust.edu.cn or pull a request directly.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.0.0
Description-Content-Type: text/markdown
Provides-Extra: test
