Comments (1)
Hi,
Thanks for your interests in our work! I think if you consider computational power as your bottleneck, you could try CondenseNets. Otherwise, you could try DenseNets.
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Related Issues (20)
- Training Time issue when training Condensenet-light on cifar100 HOT 3
- the version of pytorch HOT 1
- cuda runtime error (11) : invalid argument at /pytorch/aten/src/THC/THCGeneral.cpp:383 I have only one 2080ti,but I encounter this bug, could you give me some suggestions,please? HOT 1
- Question on CondensingConv from layers.py HOT 1
- Question on dropping function HOT 3
- Question on clamp HOT 2
- is_best defining problem
- CondeseNet-182* on Cifar100 validation 1 error rate is 19.73% where in paper is 18.47% HOT 6
- Group lasso regularization effect for ImageNet HOT 2
- dropout before convolution layer HOT 2
- condensenet-86 parameters number different from torchsummary HOT 2
- Question on CondensingLinear HOT 1
- How to run Condensenet without GPU?
- +FDC (full dense connectivity) version
- Request to update to PyTorch Version 1.6.0 (Latest) HOT 2
- Testing on ARM without CUDA and GPU
- What are the Training Arguments for ImageNet Pre-Trained Model? HOT 5
- Questions on implementation of dropping
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