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, ...
I have implemented a multiclass segmentation network, but I am confused to why the visualization of my predicted mask shows the wrong colors. As shown in the picture, the green class gets predicted to be yellow. But as the structure is correct, I don't think that it is actually ...