I need some help with my segmentation model

this is the mask, segmentation result and the input image
Is that overfitting or something else?my dice coefficient was stable at 0.9685 for about 7 epoches.

my model is consists of two Unet which I think is not complicated.The conv block looks just like this

class BasicResBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(BasicResBlock, self).__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
        self.cov1x1 = nn.Sequential(  # 调节通道数
            nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0),

    def forward(self, x):
        res = self.cov1x1(x)
        x = self.double_conv(x)
        return F.relu(res + x)

My model output 2 results,coarse and refine, and I set the loss like this

coarse_mask, refine= net(imgs)
                loss1 = criterion1(coarse_mask, true_masks)
                loss2 = criterion1(refine, true_masks)
                loss =0.8*loss1 +loss2

I really need some advice!!!Thank you!!!