This is a fork of https://github.com/machrisaa/tensorflow-vgg that is more configurable and extendable.

VGG well in classifying ImageNet data at ILSVRC 2014, winning runner-up at the competition. However, VGG can be used for many image classification tasks as well as others. To help achieve this, we add num_classes as a parameter.

Dropout is an important hyper-parameter that can greatly impact the speed of training. We also include it as a configurable parameter.

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 NPY or VGG19 NPY 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.

##Extra This library has been used in my another Tensorflow image style synethesis project: stylenet

##Update: Added a trainable version of the VGG19 vgg19_trainable. It support train from existing vaiables or from scratch. (But. the trainer is not included)

A very simple testing is added test_vgg19_trainable, switch has demo about how to train, switch off train mode for verification, and how to save.

A seperated file is added (instead of changing existing one) because I want to keep the simplicity of the original VGG networks.