Segmentation models with pretrained backbones
| Backbone model | Name | Weights | Preprocessing |
|---|---|---|---|
| VGG16 | vgg16 |
imagenet |
KA* |
| VGG19 | vgg19 |
imagenet |
KA* |
| ResNet18 | resnet18 |
imagenet |
BGR, [0, 255] |
| ResNet34 | resnet34 |
imagenet |
BGR, [0, 255] |
| ResNet50 | resnet50 |
imagenetimagenet11k-places365ch |
BGR, [0, 255] |
| ResNet101 | resnet101 |
imagenet |
BGR, [0, 255] |
| ResNet152 | resnet152 |
imagenetimagenet11k |
BGR, [0, 255] |
| ResNeXt50 | resnext50 |
imagenet |
[0, 255] |
| ResNeXt101 | resnext101 |
imagenet |
[0, 255] |
| DenseNet121 | densenet121 |
imagenet |
KA* |
| DenseNet169 | densenet169 |
imagenet |
KA* |
| DenseNet201 | densenet201 |
imagenet |
KA* |
| Inception V3 | inceptionv3 |
imagenet |
KA* |
| Inception ResNet V2 | inceptionresnetv2 |
imagenet |
KA* |
- KA - preprocessing function from keras.applications
- Python 3.6 or higher
- Keras >=2.1.0
- Tensorflow >= 1.4
$ pip install segmentation_models
$ git clone https://github.com/qubvel/segmentation_models.git
$ cd segmentation_models
$ git submodule update --init --recursiveTrain Unet model:
from segmentation_models import Unet
from segmentation_models.backbones import get_preprocessing
# prepare data
x, y = ...
preprocessing_fn = get_preprocessing('resnet34')
x = preprocessing_fn(x)
# prepare model
model = Unet(backbone_name='resnet34', encoder_weights='imagenet')
model.compile('Adam', 'binary_crossentropy', ['binary_accuracy'])
# train model
model.fit(x, y)Train FPN model:
from segmentation_models import FPN
model = FPN(backbone_name='resnet34', encoder_weights='imagenet')Freeze encoder weights for fine-tuning during first epochs of training:
from segmentation_models import FPN
from segmentation_models.utils import set_trainable
model = FPN(backbone_name='resnet34', encoder_weights='imagenet', freeze_encoder=True)
model.compile('Adam', 'binary_crossentropy', ['binary_accuracy'])
# pretrain model decoder
model.fit(x, y, epochs=2)
# release all layers for training
set_trainable(model) # set all layers trainable and recompile model
# continue training
model.fit(x, y, epochs=100)- Update Unet API
- Update FPN API
- Add Linknet models
- Add PSP models
- Add DPN backbones
Version 0.1.1
- Added
Linknetmodel - Keras 2.2+ compatibility (fixed import of
_obtain_input_shape) - Small code improvements and bug fixes
Version 0.1.0
UnetandFPNmodels
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent