SemTorch: A Semantic Segmentation library build above FastAI

Hi, guys:

I am happy to announce that I have released SemTorch.

This library allows you to train 5 different Sementation Models: UNet, DeepLabV3+, HRNet, Mask-RCNN and U²-Net in the same way.

For example:

# SemTorch
from semtorch import get_segmentation_learner

learn = get_segmentation_learner(dls=dls, number_classes=2, segmentation_type="Semantic Segmentation",
                                 architecture_name="deeplabv3+", backbone_name="resnet50", 
                                 metrics=[tumour, Dice(), JaccardCoeff()],wd=1e-2,

This library was used in my other project: Deep-Tumour-Spheroid. In this project I trained segmentation models for segmenting brain tumours.

The notebooks can be found here. They are an example of how easily is to train a model with this library. You can use SemTorch with your own datasets!

In addition, if you want to know more about this project you can go to

Deeper look in all the parameters of Semtorch
All this library is focused in this function that will get new models and options over time.

def get_segmentation_learner(dls, number_classes, segmentation_type, architecture_name, backbone_name,
                             loss_func=None, opt_func=Adam,, splitter=trainable_params, 
                             cbs=None, pretrained=True, normalize=True, image_size=None, metrics=None, 
                             path=None, model_dir='models', wd=None, wd_bn_bias=False, train_bn=True,

This function return a learner for the provided architecture and backbone


  • dls (DataLoader): the dataloader to use with the learner
  • number_classes (int): the number of clases in the project. It should be >=2
  • segmentation_type (str): just Semantic Segmentation accepted for now
  • architecture_name (str): name of the architecture. The following ones are supported: unet, deeplabv3+, hrnet, maskrcnn and u2^net
  • backbone_name (str): name of the backbone
  • loss_func (): loss function.
  • opt_func (): opt function.
  • lr (): learning rates
  • splitter (): splitter function for freazing the learner
  • cbs (List[cb]): list of callbacks
  • pretrained (bool): it defines if a trained backbone is needed
  • normalize (bool): if normalization is applied
  • image_size (int): REQUIRED for MaskRCNN. It indicates the desired size of the image.
  • metrics (List[metric]): list of metrics
  • path (): path parameter
  • model_dir (str): the path in which save models
  • wd (float): wieght decay
  • wd_bn_bias (bool):
  • train_bn (bool):
  • moms (Tuple(float)): tuple of different momentuns


  • learner: value containing the learner object

Supported configs

Architecture supported config backbones
unet Semantic Segmentation,binary Semantic Segmentation,multiple resnet18, resnet34, resnet50, resnet101, resnet152, xresnet18, xresnet34, xresnet50, xresnet101, xresnet152, squeezenet1_0, squeezenet1_1, densenet121, densenet169, densenet201, densenet161, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn, alexnet
deeplabv3+ Semantic Segmentation,binary Semantic Segmentation,multiple resnet18, resnet34, resnet50, resnet101, resnet152, resnet50c, resnet101c, resnet152c, xception65, mobilenet_v2
hrnet Semantic Segmentation,binary Semantic Segmentation,multiple hrnet_w18_small_model_v1, hrnet_w18_small_model_v2, hrnet_w18, hrnet_w30, hrnet_w32, hrnet_w48
maskrcnn Semantic Segmentation,binary resnet50
u2^net Semantic Segmentation,binary small, normal

Thanks for sharing :slight_smile: