This is a Tensorflow implemention of VGG 16 and VGG 19 based on tensorflow-vgg16 and Caffe to Tensorflow. Original Caffe implementation can be found in here and here.
We have modified the implementation of tensorflow-vgg16 to use numpy loading instead of default tensorflow model loading in order to speed up the initialisation and reduce the overall memory usage. This implementation enable further modify the network, e.g. remove the FC layers, or increase the batch size.
To use the VGG networks, the npy files for VGG16 or VGG19 has to be downloaded.
##Usage Use this to build the VGG object
vgg = vgg19.Vgg19()
vgg.build(images)
or
vgg = vgg16.Vgg16()
vgg.build(images)
The images is a tensor with shape [None, 224, 224, 3].
Trick: the tensor can be a placeholder, a variable or even a constant.
All the VGG layers (tensors) can then be accessed using the vgg object. For example, vgg.conv1_1, vgg.conv1_2, vgg.pool5, vgg.prob, ...
test_vgg16.py and test_vgg19.py contain the sample usage.