Version 0.2.1
- Added
set_regularization
function - Added
beta
argument to dice loss - Added
threshold
argument for metrics - Fixed
prerprocess_input
for mobilenets - Fixed missing parameter
interpolation
inResizeImage
layer config - Some minor improvements in docs, fixed typos
Version 0.2.0
- New backbones (SE-ResNets, SE-ResNeXts, SENet154, MobileNets)
- Metrcis:
iou_score
/jaccard_score
f_score
/dice_score
- Losses:
jaccard_loss
bce_jaccard_loss
cce_jaccard_loss
dice_loss
bce_dice_loss
cce_dice_loss
- Documentation Read the Docs
- Tests + Travis-CI
- Some parameters renamed (see API docs)
encoder_freeze=True
does notfreeze
BatchNormalization layer of encoder
@IlyaOvodov #15 #37 investigation of align_corners
parameter in ResizeImage
layer
@NiklasDL #29 investigation about convolution kernel in PSPNet final layers
Version 0.1.2
- Added PSPModel
- Prepocessing functions for all backbones:
from segmentation_models.backbones import get_preprocessing
preprocessing_fn = get_preprocessing('resnet34')
X = preprocessing_fn(x)
- Default param
use_batchnorm=True
for all decoders - FPN model
Upsample2D
layer renamed toResizeImage
Version 0.1.1
- Added
Linknet
model - Keras 2.2+ compatibility (fixed import of
_obtain_input_shape
) - Small code improvements and bug fixes
Version 0.1.0
Unet
andFPN
models