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If I would like to use VGG19 instead of VGG16, where should I change the code?
ghost updated
7 years ago
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Definition of a model leveraging transfer learning, in this case using the VGG19 model.
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now Chainer supports VGG19, so how about below two?
- adding VGG19 to chainercv/links/model/vgg
- adding its link to caffemodel to examples/vgg/caffe2npz.py
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vgg19_bn_mask "state_dict = torch.load('/content/drive/MyDrive/checkpoints/vgg_19_bn/vgg19_bn_trail-2_2021Aug14_22.47')['net']"error
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Does this repo support VGG19 ? Since it's very close to VGG16? If yes, what should I change?
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Can some one share VGG19 architecture, modified to run with facenet code?
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Hi @cl199443
please helping me , thanks!
Traceback (most recent call last):
File "", line 10, in
File "/home/ubuntu/Deep-Semantic-Feature-Matching-master/vgg19.py", line 20, in __init__
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LeNet-5, AlexNet, GoogleLeNet, VGGNet, ResNet-34, ResNet-50, Xception, SENet
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Is there a reason why discriptor and detector used VGG19 network architecture? Not using ResNet or other network architecture
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I'm confused whether we need to update the vgg network. The [example](https://github.com/pytorch/examples/tree/master/fast_neural_style) in pytorch tutorial keeps the parameters in vgg16 untouched. Bu…