Comments (1)
If you run main.py
on a machine with a single GPU it will train using only that one GPU. If you plan on training on ImageNet-size images (3x224x224) you will also need to reduce the batch size to 128, 64, or even 32, since you won't be able to fit a batch size of 2048 into a single GPU.
When using such a small batch size, it also might make sense to replace LARS by SGD.
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Related Issues (20)
- Issue loading checkpoint.pth file HOT 1
- Augmentation Distribution HOT 1
- Will BarlowTwins overfit on the training data?
- When using the own dataset, loss is around 10,000 HOT 8
- About the last normalization layer HOT 1
- Dose we can add distorted image, like Y_a, Y_b, Y_c?
- A question on the BT loss with Batch Norm layers HOT 4
- Start index for each epoch HOT 3
- weight update for step=0,epoch=0 get's missed. HOT 2
- Applications on one-dimensional signal datasets HOT 4
- Where could we find the "reproduced version" of the other SSL methods HOT 1
- Question about Fig. 4 in the paper HOT 4
- Quality of Embeddings
- Possible bug on the loss computation HOT 1
- Error in saving resnet50.pth HOT 2
- efficiency proposal HOT 1
- NaN's introduced during training.
- providing the linear ImageNet classifier weights
- Pre-training model for CIFAR
- Why c.div_(self.args.batch_size) is needed? HOT 1
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