54 lines
1.9 KiB
Markdown
54 lines
1.9 KiB
Markdown
# FMRI example models
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The following sections describe the models included in this repository,
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with attribution. The repository also includes deinplaced versions of
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the models, where relevant.
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## alexnet
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AlexNet trained on ILSVRC 2012, almost exactly as described in ImageNet
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classification with deep convolutional neural networks by Krizhevsky et
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al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
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## caffenet
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AlexNet trained on ILSVRC 2012, with a minor variation from the version
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as described in ImageNet classification with deep convolutional neural
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networks by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue
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@jeffdonahue)
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## vgg-16
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The model is an improved version of the 16-layer model used by the VGG
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team in the ILSVRC-2014 competition. The details can be found in the
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following [arXiv paper](http://arxiv.org/pdf/1409.1556):
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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K. Simonyan, A. Zisserman
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arXiv:1409.1556
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Please cite the paper if you use the model.
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In the paper, the model is denoted as the configuration `D` trained with
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scale jittering. The input images should be zero-centered by mean pixel
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(rather than mean image) subtraction. Namely, the following BGR values
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should be subtracted: `[103.939, 116.779, 123.68]`.
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## vgg-19
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The model is an improved version of the 19-layer model used by the VGG
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team in the ILSVRC-2014 competition. The details can be found in the
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following [arXiv paper](http://arxiv.org/pdf/1409.1556):
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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K. Simonyan, A. Zisserman
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arXiv:1409.1556
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Please cite the paper if you use the model.
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In the paper, the model is denoted as the configuration `E` trained with
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scale jittering. The input images should be zero-centered by mean pixel
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(rather than mean image) subtraction. Namely, the following BGR values
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should be subtracted: `[103.939, 116.779, 123.68]`.
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