change_detection.pytorch
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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. ๐
๐ฑ How to use
Please refer to local_test.py temporarily.
๐ญ Models
Architectures
-
[x] Unet [paper]
-
[x] Unet++ [paper]
-
[x] MAnet [paper]
-
[x] Linknet [paper]
-
[x] FPN [paper]
-
[x] PSPNet [paper]
-
[x] PAN [paper]
-
[x] DeepLabV3 [paper]
-
[x] DeepLabV3+ [paper]
-
[x] UPerNet [paper]
-
[x] 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 | 466M | |
resnext101_32x48d | 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
- [x] LEVIR-CD
- [x] SVCD [google drive | baidu disk (x8gi)]
- [ ] ...
๐ 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
โกโกโก I am trying to build this project, if you are interested, don't hesitate to join us!
๐ฏ๐ฏ๐ฏ Contact me at [email protected] or pull a request directly or join our WeChat group.
