Comments (5)
That is what i need now,could you please provide more pretrained models,such as oct_resnet152. Thank you very much.
from octconv.pytorch.
@jujsd Sorry to tell but now my available GPU resource is too limited to train such a resource-consuming model. Welcome to any PR using my provided model definition in this repo.
from octconv.pytorch.
Thank u for response. In addition,i have a problem,the speed of OctResnet-50 is actually slower than original Resnet-50. Do you have any idea about it? Is OctResnet-50 more faster in your experiments?
from octconv.pytorch.
It's actually slower in my experiments too. Maybe due to the inefficient implementation on GPU now (refer to #4 ).
from octconv.pytorch.
It's actually slower in my experiments too. Maybe due to the inefficient implementation on GPU now (refer to #4 ).
Ok,thank u again
from octconv.pytorch.
Related Issues (20)
- Initialisation of layers and weights not available HOT 1
- about the pre-trained model HOT 1
- basic_block in oct_resnet HOT 1
- redundant x_h in oct_resnet ? HOT 1
- There is no main script to run the network ?
- There are implementation with UNet? HOT 1
- train coed HOT 1
- about the pre-trained model of the OctResNet-101 HOT 3
- Could you add a example of resnet18? HOT 1
- Difference between your implementation and paper. HOT 3
- BatchNorm before activation vs BatchNorm after activation
- Why only return high frequency features (x_h) HOT 1
- Could you please provide the training recipe file?
- Training code HOT 2
- Why is original resnet50 faster than octave-resnet50? HOT 4
- hi,Why the accuracy of original resnet26 is higher than octave-resnet26 used in cifar10 and cifar100? HOT 2
- octave conv cannot support input at any size HOT 3
- Hi, why not adopting pre-actice version? HOT 1
- Computation time around 3 times longer with OctConv HOT 3
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