Thanks your reply!
So basically it would comment out = F.sigmoid(out) and placing the loss with
out = nn.BCEWithLogitsLoss(out) ? .
Also I was considering that the very low decrease of the loss is due to my images that are RGB but not normalized (like to resnet values or with their std and avg).
Currently I’m loading my data by using the following code:
class MyDataset_test(Dataset):
def init(self, image_paths ,train=True):
self.image_paths = image_paths
#self.mask_paths = mask_paths
    def transforms(self, image, mask):
        #img = img.resize((wsize, baseheight), PIL.Image.ANTIALIAS)
        #image = transforms.Resize(size=(64, 64))(image)
        #mask = transforms.Resize(size=(64, 64))(mask)
        image = image.resize((64, 64), PIL.Image.NEAREST)
        mask = mask.resize((64, 64), PIL.Image.NEAREST)
        image = TF.to_tensor(image)
        mask = TF.to_tensor(mask)
        
        return  mask
    
    def __getitem__(self, index):
        #image = Image.open(self.image_paths[index])
        mask = Image.open(self.mask_paths[index])
        
        x, y = self.transforms(mask)
        return x, y
    
    def __len__(self):
        return len(self.image_paths)

maybe this could be a possible issue.
Thanks in advance!