/change_detection.pytorch

Deep learning models for change detection of remote sensing images

Primary LanguagePythonMIT LicenseMIT

Change Detection Models

Python library with Neural Networks for Change Detection based on PyTorch.

model architecture

This project is inspired by segmentation_models.pytorch and built based on it. 😄

🌱 How to use

Please refer to local_test.py temporarily.

🔭 Models

Architectures

Encoders

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).

ResNet
Encoder Weights Params, M
resnet18 imagenet / ssl / swsl 11M
resnet34 imagenet 21M
resnet50 imagenet / ssl / swsl 23M
resnet101 imagenet 42M
resnet152 imagenet 58M
ResNeXt
Encoder Weights Params, M
resnext50_32x4d imagenet / ssl / swsl 22M
resnext101_32x4d ssl / swsl 42M
resnext101_32x8d imagenet / instagram / ssl / swsl 86M
resnext101_32x16d instagram / ssl / swsl 191M
resnext101_32x32d instagram 466M
resnext101_32x48d instagram 826M
ResNeSt
Encoder Weights Params, M
timm-resnest14d imagenet 8M
timm-resnest26d imagenet 15M
timm-resnest50d imagenet 25M
timm-resnest101e imagenet 46M
timm-resnest200e imagenet 68M
timm-resnest269e imagenet 108M
timm-resnest50d_4s2x40d imagenet 28M
timm-resnest50d_1s4x24d imagenet 23M
Res2Ne(X)t
Encoder Weights Params, M
timm-res2net50_26w_4s imagenet 23M
timm-res2net101_26w_4s imagenet 43M
timm-res2net50_26w_6s imagenet 35M
timm-res2net50_26w_8s imagenet 46M
timm-res2net50_48w_2s imagenet 23M
timm-res2net50_14w_8s imagenet 23M
timm-res2next50 imagenet 22M
RegNet(x/y)
Encoder Weights Params, M
timm-regnetx_002 imagenet 2M
timm-regnetx_004 imagenet 4M
timm-regnetx_006 imagenet 5M
timm-regnetx_008 imagenet 6M
timm-regnetx_016 imagenet 8M
timm-regnetx_032 imagenet 14M
timm-regnetx_040 imagenet 20M
timm-regnetx_064 imagenet 24M
timm-regnetx_080 imagenet 37M
timm-regnetx_120 imagenet 43M
timm-regnetx_160 imagenet 52M
timm-regnetx_320 imagenet 105M
timm-regnety_002 imagenet 2M
timm-regnety_004 imagenet 3M
timm-regnety_006 imagenet 5M
timm-regnety_008 imagenet 5M
timm-regnety_016 imagenet 10M
timm-regnety_032 imagenet 17M
timm-regnety_040 imagenet 19M
timm-regnety_064 imagenet 29M
timm-regnety_080 imagenet 37M
timm-regnety_120 imagenet 49M
timm-regnety_160 imagenet 80M
timm-regnety_320 imagenet 141M
GERNet
Encoder Weights Params, M
timm-gernet_s imagenet 6M
timm-gernet_m imagenet 18M
timm-gernet_l imagenet 28M
SE-Net
Encoder Weights Params, M
senet154 imagenet 113M
se_resnet50 imagenet 26M
se_resnet101 imagenet 47M
se_resnet152 imagenet 64M
se_resnext50_32x4d imagenet 25M
se_resnext101_32x4d imagenet 46M
SK-ResNe(X)t
Encoder Weights Params, M
timm-skresnet18 imagenet 11M
timm-skresnet34 imagenet 21M
timm-skresnext50_32x4d imagenet 25M
DenseNet
Encoder Weights Params, M
densenet121 imagenet 6M
densenet169 imagenet 12M
densenet201 imagenet 18M
densenet161 imagenet 26M
Inception
Encoder Weights Params, M
inceptionresnetv2 imagenet / imagenet+background 54M
inceptionv4 imagenet / imagenet+background 41M
xception imagenet 22M
EfficientNet
Encoder Weights Params, M
efficientnet-b0 imagenet 4M
efficientnet-b1 imagenet 6M
efficientnet-b2 imagenet 7M
efficientnet-b3 imagenet 10M
efficientnet-b4 imagenet 17M
efficientnet-b5 imagenet 28M
efficientnet-b6 imagenet 40M
efficientnet-b7 imagenet 63M
timm-efficientnet-b0 imagenet / advprop / noisy-student 4M
timm-efficientnet-b1 imagenet / advprop / noisy-student 6M
timm-efficientnet-b2 imagenet / advprop / noisy-student 7M
timm-efficientnet-b3 imagenet / advprop / noisy-student 10M
timm-efficientnet-b4 imagenet / advprop / noisy-student 17M
timm-efficientnet-b5 imagenet / advprop / noisy-student 28M
timm-efficientnet-b6 imagenet / advprop / noisy-student 40M
timm-efficientnet-b7 imagenet / advprop / noisy-student 63M
timm-efficientnet-b8 imagenet / advprop 84M
timm-efficientnet-l2 noisy-student 474M
timm-efficientnet-lite0 imagenet 4M
timm-efficientnet-lite1 imagenet 5M
timm-efficientnet-lite2 imagenet 6M
timm-efficientnet-lite3 imagenet 8M
timm-efficientnet-lite4 imagenet 13M
MobileNet
Encoder Weights Params, M
mobilenet_v2 imagenet 2M
timm-mobilenetv3_large_075 imagenet 1.78M
timm-mobilenetv3_large_100 imagenet 2.97M
timm-mobilenetv3_large_minimal_100 imagenet 1.41M
timm-mobilenetv3_small_075 imagenet 0.57M
timm-mobilenetv3_small_100 imagenet 0.93M
timm-mobilenetv3_small_minimal_100 imagenet 0.43M
DPN
Encoder Weights Params, M
dpn68 imagenet 11M
dpn68b imagenet+5k 11M
dpn92 imagenet+5k 34M
dpn98 imagenet 58M
dpn107 imagenet+5k 84M
dpn131 imagenet 76M
VGG
Encoder Weights Params, M
vgg11 imagenet 9M
vgg11_bn imagenet 9M
vgg13 imagenet 9M
vgg13_bn imagenet 9M
vgg16 imagenet 14M
vgg16_bn imagenet 14M
vgg19 imagenet 20M
vgg19_bn imagenet 20M

🚚 Dataset

🏆 Competitions won with the library

change_detection.pytorch has competitiveness and potential in the change detection competitions. Here you can find competitions, names of the winners and links to their solutions.

📃 Citing 

@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}}
}

📚 Reference

📫 Contact

⚡⚡⚡ 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 or join our WeChat group.

wechat group