Comments (4)
It is a little hard to decide. For training without using SSL, I find stopping at 80000 iterations is best. When I continue to train with more iterations, overfitting will be caused. For training with SSL, I find there is not overfitting, but after 120000 iterations the mIoU starts to be stable. So I choose to stop at 120000 iterations for SSL.
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In my opinion, when training without SSL, you tested every snapshot on 'val' split of Cityscapes and set the best snapshot iteration as the "Early Stopping" hyper-parameter.
from bdl.
Not really. The best result is always shown when iteration is around 80000. It is unnecessary to validate all snapshots.
from bdl.
But if you have not got the performance of 75000 , 85000, 90000, how did you know that the best results is shown around 80000.
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Related Issues (20)
- Complete training procedure HOT 1
- Train CycleGAN HOT 2
- First Image Transltation HOT 6
- About the implementation HOT 1
- Number of common classes in Synthia dataset HOT 2
- About SYNTHIA to Cityscapes HOT 1
- Bug in the DeepLab ResNet101 atrous convolution forward function HOT 3
- does BDL support for pytorch 1.7 and cuda 10.2? HOT 1
- how to train own custom dataset? HOT 3
- error at running train.py for cyclegan HOT 2
- Inconsistency fix from CycleGan repository HOT 1
- problem about loss become nan HOT 1
- fineSize (cropSize) for Cycle-Gan HOT 5
- quick question HOT 5
- Issues with reproducing results using pre-trained weights on PyTorch 1.7 and CUDA 11 HOT 4
- About confidence threshold in SSL HOT 2
- Color of pseudolabels HOT 1
- Question about train,thanks
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