Comments (3)
I believe they are equivalent because 1) the model built using B) has the exact same number of parameters as that with C). C) is a a more succinct implementation, bit acheives the same thing.
However, if one tries to port weights from the torch code to Keras, they would find the lambda layer would fit, whereas this would not. I will look into, and update my code to match the lambda layer version when possible, as weight translation would be easier.
from keras-resnext.
Interesting thank you so much! Just making sure I understand all this code that is going on. It seems the lambda layer is indeed doing what item c in fig 3 is displaying. Awesome to be on the right track! Thanks again!
from keras-resnext.
just find this page: microsoft/MMdnn#58, may help with the weights convertion.
from keras-resnext.
Related Issues (20)
- IMAGENET_TF_WEIGHTS_PATH isn't defined in resnext.py HOT 2
- Is the model structure in the code the same as in the paper? HOT 1
- Hello HOT 2
- ImportError: cannot import name '_obtain_input_shape'? HOT 5
- Grouped Convolution Block
- Parameter Count
- Where can i download the weight? HOT 1
- Incorrect number of features at last stage for ResneXt-101
- Where is ImageNet Weights file? HOT 5
- Add a license
- Do you have plan of implementing
- Pull request pending
- `ResNeXt` != `ResNext`
- What's the best Optimizer to use and what default parameters go well. HOT 1
- cannot import name 'ResNeXt'` HOT 4
- Number of parameters HOT 1
- Running out of Memory HOT 1
- Different Implementation from paper and original code HOT 2
- Where can I download the pretained model based on Imagenet? HOT 7
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from keras-resnext.