winterrykim / change_detection.pytorch

Deep learning models for change detection of remote sensing images

Change Detection Models

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

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

• Unet [paper]

• Unet++ [paper]

• MAnet [paper]

• Linknet [paper]

• FPN [paper]

• PSPNet [paper]

• PAN [paper]

• DeepLabV3 [paper]

• DeepLabV3+ [paper]

• UPerNet [paper]

• STANet [paper]

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

• LEVIR-CD
• SVCD [google drive | baidu disk (x8gi)]
• ...

🏆 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

• qubvel/segmentation_models.pytorch
• albumentations-team/albumentations
• open-mmlab/mmsegmentation
• wenhwu/awesome-remote-sensing-change-detection

📫 Contact

⚡⚡⚡ I am trying to build this project, if you are interested, don't hesitate to join us!

👯👯👯 Contact me at [e-mail] or pull a request directly or join our WeChat group.