Comments (1)
I haven't tested it yet. I am working on a practical image classifier example with resnet50. I've discussed this in #2. I will include a speed benchmark.
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Related Issues (20)
- inconsistency with the original paper HOT 9
- torch.jit support HOT 6
- same results as traditional dropout if block_size=1 HOT 1
- How to put in evaluation mode? HOT 1
- DropBlock1D HOT 3
- DropBlock at Residual Connections HOT 1
- resnet-cifar10.py DropBlock scheduler HOT 1
- Is it needed to somehow turn off Dropblock layer during inference time? HOT 1
- Pytorch Lightning as a back-end HOT 2
- Where to put the dropblock in the ResNet network? HOT 1
- The examples for using dropblock is different? HOT 1
- why drop_prob in config.yaml is 0 HOT 1
- Pre-trained ResNet50 model on ImageNet? HOT 1
- Would you please tell me how did you make certain the positive area of the mask keep still after max pooling operation ? HOT 6
- Can I use with torch.no_grad() in the forward of dropblock.py? HOT 4
- Use in segmentation. You suggest only for convolution feature extraction layer only. What do you mean? HOT 2
- Dropblock and python version HOT 3
- 'ResNetCustom' object has no attribute '_norm_layer' HOT 2
- make dropblock run faster HOT 1
- shared DropBlock mask across different feature channels
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