Comments (1)
We use single V100 for training.
From my personal experience, it is impossible for us to reproduce exactly the same results. Hardware and software differences can have an impact on the results. It is okay if the results are slightly different.
from semi-supervised-learning.
Related Issues (20)
- Why are only lb_data and lb_target used in 'Create dataset' section in "Customize datasets" tutorial HOT 4
- New feature request: Hope can compatible with Apple M1 / M2 chip. HOT 2
- Question: How to feed the model with tabular data? HOT 2
- Possibility to eval on one single data HOT 3
- Aim repository path is hard-coded HOT 1
- Regarding the bug that model_best.pth failed to save HOT 4
- About warmup does not seem to take effect HOT 3
- Checkpointing based on other evaluation metrics HOT 4
- Reduce the complexity of dependencies HOT 2
- Tensorboard log is only updated when the model is evaluated
- [Question] Regarding the Unused m Parameter in the RandAugment Class. HOT 2
- The problem with train_sampler HOT 3
- Resume Aim tracking when resuming training
- Consult about the custom network usage in the USB HOT 2
- Differences between the original paper and "results\classic_cv.csv". HOT 3
- Question about the weights in Softmatch
- "Does your algorithm support semi-supervised segmentation tasks? HOT 4
- Evaluation of STL10 (USB_CV) HOT 1
- Hard coding the dataset path HOT 5
- Unable to run eval.py on USB NLP Datasets HOT 3
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from semi-supervised-learning.