1. Create segmentation model
Segmentation model is just a PyTorch nn.Module, which can be created as easy as:
import segmentation_models_pytorch as smp
model = smp.Unet(
encoder_name="resnet34", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
encoder_weights="imagenet", # use `imagenet` pre-trained weights for encoder initialization
in_channels=1, # model input channels (1 for gray-scale images, 3 for RGB, etc.)
classes=3, # model output channels (number of classes in your dataset)
)
- see table with available model architectures
- see table with avaliable encoders and its corresponding weights
2. Configure data preprocessing
All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give your better results (higher metric score and faster convergence). But it is relevant only for 1-2-3-channels images and not necessary in case you train the whole model, not only decoder.
from segmentation_models_pytorch.encoders import get_preprocessing_fn
preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
3. Congratulations! 🎉
You are done! Now you can train your model with your favorite framework!