Comments (7)
What is mIoU when you say "normal"?
I am training
from bisenet.
I have got 0.38 mIoU
from bisenet.
What is mIoU when you say "normal"?
I am training
0.56 miou
from bisenet.
What is mIoU when you say "normal"?
I am training0.56 miou
Did you change any hyper-parameters? Or just keep them original?
from bisenet.
What is mIoU when you say "normal"?
I am training0.56 miou
Did you change any hyper-parameters? Or just keep them original?
Below is my hyperparameter settings:
if __name__ == '__main__':
params = [
'--num_epochs', '300',
'--data', 'data/CamVid',
'--learning_rate', '0.001',
'--num_classes', '32',
'--num_workers', '6',
'--batch_size', '4',
'--crop_height', '640',
'--crop_width', '640',
'--checkpoint_step', '5',
'--context_path', 'resnet34'
]
I use the resnet34 as my Basemodel and add Kaiming initialization,
for example:
class Spatial_path(torch.nn.Module):
def __init__(self):
super().__init__()
self.convblock1 = ConvBlock(in_channels=3, out_channels=64)
self.convblock2 = ConvBlock(in_channels=64, out_channels=128)
self.convblock3 = ConvBlock(in_channels=128, out_channels=256)
self.init_weight()
def forward(self, input):
x = self.convblock1(input)
x = self.convblock2(x)
x = self.convblock3(x)
return x
def init_weight(self):
for ly in self.children():
if isinstance(ly, nn.Conv2d):
nn.init.kaiming_normal_(ly.weight, a=1)
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
The settings of other modules are the same as this one.
I am not sure if these operations will help you.
from bisenet.
What is mIoU when you say "normal"?
I am training0.56 miou
Did you change any hyper-parameters? Or just keep them original?
Below is my hyperparameter settings:
if __name__ == '__main__': params = [ '--num_epochs', '300', '--data', 'data/CamVid', '--learning_rate', '0.001', '--num_classes', '32', '--num_workers', '6', '--batch_size', '4', '--crop_height', '640', '--crop_width', '640', '--checkpoint_step', '5', '--context_path', 'resnet34' ]
I use the resnet34 as my Basemodel and add Kaiming initialization,
for example:
class Spatial_path(torch.nn.Module): def __init__(self): super().__init__() self.convblock1 = ConvBlock(in_channels=3, out_channels=64) self.convblock2 = ConvBlock(in_channels=64, out_channels=128) self.convblock3 = ConvBlock(in_channels=128, out_channels=256) self.init_weight() def forward(self, input): x = self.convblock1(input) x = self.convblock2(x) x = self.convblock3(x) return x def init_weight(self): for ly in self.children(): if isinstance(ly, nn.Conv2d): nn.init.kaiming_normal_(ly.weight, a=1) if not ly.bias is None: nn.init.constant_(ly.bias, 0)
The settings of other modules are the same as this one.
I am not sure if these operations will help you.
Thank you for your help. I would like to have a try.
from bisenet.
I have got a similar result on the main page.
However, my mIoU is 0.366. ;)
from bisenet.
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