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