Segmentation models with pretrained backbones
| Backbone model | Name | Weights | UNet | FPN |
|---|---|---|---|---|
| VGG16 | vgg16 |
imagenet |
+ | - |
| VGG19 | vgg19 |
imagenet |
+ | - |
| ResNet18 | resnet18 |
imagenet |
+ | + |
| ResNet34 | resnet34 |
imagenet |
+ | + |
| ResNet50 | resnet50 |
imagenetimagenet11k-place365ch |
+ | + |
| ResNet101 | resnet101 |
imagenet |
+ | + |
| ResNet152 | resnet152 |
imagenetimagenet11kimagenet11k-place365ch |
+ | + |
| ResNeXt50 | resnext50 |
imagenet |
+ | + |
| ResNeXt101 | resnext101 |
imagenet |
+ | + |
| DenseNet121 | densenet121 |
imagenet |
+ | + |
| DenseNet169 | densenet169 |
imagenet |
+ | + |
| DenseNet201 | densenet201 |
imagenet |
+ | + |
| Inception V3 | inceptionv3 |
imagenet |
+ | + |
| Inception ResNet V2 | inceptionresnetv2 |
imagenet |
+ | + |
- Clone repositoriy to your project
$ git clone https://github.com/qubvel/segmentation_models.git- Update submodules
$ cd segmentation_models
$ git submodule update --init --recursiveTrain Unet model:
from segmentation_models import Unet
# prepare data
x, y = ...
# prepare model
model = Unet(backbone_name='resnet34', encoder_weigths='imagenet')
model.compile('Adam', 'binary_crossentropy', ['binary_accuracy'])
# train model
model.fit(x, y)- Update Unet API
- Update FPN API
- Add Linknet models
- Add PSP models
- Add DPN backbones