Version 1.0.0
- Support for
kerasandtf.keras - Losses as classes, base loss operations (sum of losses, multiplied loss)
- NCHW and NHWC support
- Removed pure tf operations to work with other keras backends
- Reduced a number of custom objects for better models serialization and deserialization
- New backbones: EfficentNetB[0-7]
- New loss function: Focal loss
- New metrics: Precision, Recall
get_preprocessingmoved fromsm.backbones.get_preprocessingtosm.get_preprocessing
Version 0.2.1
- Added
set_regularizationfunction - Added
betaargument to dice loss - Added
thresholdargument for metrics - Fixed
prerprocess_inputfor mobilenets - Fixed missing parameter
interpolationinResizeImagelayer config - Some minor improvements in docs, fixed typos
Version 0.2.0
- New backbones (SE-ResNets, SE-ResNeXts, SENet154, MobileNets)
- Metrcis:
iou_score/jaccard_scoref_score/dice_score
- Losses:
jaccard_lossbce_jaccard_losscce_jaccard_lossdice_lossbce_dice_losscce_dice_loss
- Documentation Read the Docs
- Tests + Travis-CI
- Some parameters renamed (see API docs)
encoder_freeze=Truedoes notfreezeBatchNormalization 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=Truefor all decoders - FPN model
Upsample2Dlayer renamed toResizeImage
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