change-detection-pytorch 0.1.4

Change detection models with pre-trained backbones. Inspired by segmentation_models.pytorch.

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

:truck: 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.

:page_with_curl: 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}}
}

:books: Reference

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

:mailbox: 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.