Closed wokeyide1999 closed 3 years ago
我在数据集DUTS上进行10000次迭代的情况下,分别进行了无Fold-ASPP模块和有Fold-ASPP模块的训练; 进行无Fold-ASPP的修改为将self.dem1修改为如下代码 self.dem1 = nn.Sequential(nn.Conv2d(2048, 512, kernel_size=3, padding=1), nn.BatchNorm2d(512), nn.PReLU()) 同时将self.layer4的stride调整为2 self.layer4 = self._make_layer(block1, 512, layers[3], stride=2)
self.dem1 = nn.Sequential(nn.Conv2d(2048, 512, kernel_size=3, padding=1), nn.BatchNorm2d(512), nn.PReLU())
self.layer4 = self._make_layer(block1, 512, layers[3], stride=2)
我的预期效果是有Fold-ASPP的训练好于无Fold-ASPP的训练效果,但结果恰好相反,请问可能的原因是什么?(训练得到的测试结果如下) 迭代10000次,无fold-ASPP
DUTS-TE MAE: 0.0511 maxF: 0.8533 avgF: 0.7539 wfm: 0.7512 Sm: 0.8570 Em: 0.8677
迭代10000次,有Fold-ASPP
DUTS-TE MAE: 0.0526 maxF: 0.8468 avgF: 0.7622 wfm: 0.7505 Sm: 0.8523 Em: 0.8735
@wokeyide1999 迭代10000次? 起码要40epoch or 100000 次~
明白了,多谢
我在数据集DUTS上进行10000次迭代的情况下,分别进行了无Fold-ASPP模块和有Fold-ASPP模块的训练; 进行无Fold-ASPP的修改为将self.dem1修改为如下代码
self.dem1 = nn.Sequential(nn.Conv2d(2048, 512, kernel_size=3, padding=1), nn.BatchNorm2d(512), nn.PReLU())
同时将self.layer4的stride调整为2self.layer4 = self._make_layer(block1, 512, layers[3], stride=2)
我的预期效果是有Fold-ASPP的训练好于无Fold-ASPP的训练效果,但结果恰好相反,请问可能的原因是什么?(训练得到的测试结果如下) 迭代10000次,无fold-ASPP
DUTS-TE MAE: 0.0511 maxF: 0.8533 avgF: 0.7539 wfm: 0.7512 Sm: 0.8570 Em: 0.8677
迭代10000次,有Fold-ASPP
DUTS-TE MAE: 0.0526 maxF: 0.8468 avgF: 0.7622 wfm: 0.7505 Sm: 0.8523 Em: 0.8735