Pytorch-Segmentation-multi-models Pytorch implementation for Semantic Segmentation with multi models for blood vessel segmentation in fundus images of DRIVE dataset. Deeplabv3, Deeplabv3_plus, PSPNet, UNet, UNet_AutoEncoder, UNet_nested, R2AttUNet, AttentionUNet, RecurrentUNet, SEGNet, CENet, DsenseAS...
import segmentation_models_pytorch as smp # lets assume we have multilabel prediction for 3 classes output = torch.rand([10, 3, 256, 256]) target = torch.rand([10, 3, 256, 256]).round().long() # first compute statistics for true positives, false positives, false negative and # true...
If what I'm thinking is right, multiclass is different from multilabel and multiclass assumes that there are multiple classes this segmentation pixels could belong to, but it will be only one class per each pixel, and multilabel assumes that those pixels have more than one class per each, ...