Comments (3)
@PkuRainBow If I remember correctly; the results are quite similar without the need for sync bn.
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Hi @PkuRainBow, thank you for your interest in our work.
This error is at the end of the first epoch, where in the last batch, you have only one image and BN gives an error, to solve this, please simply set drop_last = True
in the dataloaders (or change the batch size / do an if statement to skip the last step if BZ = 1).
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@yassouali Thanks for your quick reply. I am wondering whether you have compared the final performance of the method trained with 1x 32G V10 GPU and the method trained with 2x 16G V100 GPUs as we want to accelerate the training time.
Especially, we are not sure whether SyncBN is necessary when using 2x GPUs.
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Related Issues (20)
- Unsupervised data is only used to improve the performance of the Encoder part? HOT 1
- Labels for unsupervised HOT 1
- Cusom dataset with one class HOT 2
- adjusting the model for input images with 4 channels HOT 3
- Training model only on foreground labels HOT 3
- inference with 4-channel model HOT 7
- Adjust the number of decoders HOT 2
- checkerboard HOT 7
- How can I train this model using my own dataset? HOT 1
- About checkpoint HOT 1
- About loss_unsup
- dataloader HOT 1
- Errors while inferencing the trained model HOT 1
- What are the required dimensions of `predict` and `target` for abCE loss? HOT 2
- Question about the paper "Semi-Supervised Semantic Segmentation with Cross-Consistency Training" HOT 1
- Program error during training HOT 1
- Training on custom datasets in CCT. HOT 4
- Question about the plot of Figure 2 in the paper HOT 1
- 自定义数据集问题
- Why use MSE instead of CE and KL divergence
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