Comments (2)
nuoran7607
commented on June 26, 2024
1
One possible reason is that most of the time is spent on reading images. Since there are so many images in ImageNet, it is best to use linux multiprocessing during training and testing (i.e. workers parameter is not set to 0, normal linux systems support workers parameter as 8). Another solution is to put training and test images on a solid-state drive, which can greatly increase the speed of reading images compared to a mechanical hard drive
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InstantWindy
commented on June 26, 2024
I did not read the ImageNet dataset, but read the Cifar dataset
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Related Issues (20)
- loss question HOT 4
- Single GPU Training HOT 3
- Loss descent slowly HOT 1
- Checkpoints with 50 epochs HOT 1
- [Problem] problem occured when trained on custom dataset HOT 4
- difference between byol in table19 and simsiam? HOT 1
- larger batch size with linear scale does not work
- checkpoint for epoch 800
- Slow convergence with SGD linear evaluation HOT 1
- SyncBatchnorm usage in main_lincls.py HOT 2
- About the projection and prediction head dimension configs
- Loss collapse during training HOT 1
- About the shape of image[0] and image[1] HOT 1
- Pre-trained weights for SimCLR, MoCov2, BYOL, SwaV HOT 1
- The value of loss function nn.CosineSimilarity is negative HOT 1
- self.encoder.fc in builder.py
- which vector is used in experiment (the vector atfer predictor or after projection)?
- Could not find any class folder
- Loss increasing
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