name | caffemodel | caffemodel_url | sha1 | gist_id |
---|---|---|---|---|
Fully convolutional reduced VGGNet |
VGG_ILSVRC_16_layers_fc_reduced.caffemodel |
97eb7c469c5097f51a0f9a944f4a5731f470eee2 |
This is a model used in the paper
ParseNet: Looking Wider to See Better
Wei Liu, Andrew Rabinovich, Alexander C. Berg
arXiv:1506.04579
This is a network modified from VGGNet by making it fully convolutional and also by subsampling parameters from fc6 and fc7 layers. This is useful when using it to finetune for segmentation. For example, ParseNet shows how to use it to finetune for semantic segmentation task.
@idanusher I guess an average global pooling is needed for the final probability. For 16 x 16 feature maps, avg. global pooling will average all entries in the feature map and return one single value. You can check this in the Caffe Pooling layer.