原文链接：http://www.cs.toronto.edu/~frossard/post/vgg16/
VGG in TensorFlow
Model and pre-trained parameters for VGG16 in TensorFlow
17 Jun 2016, 01:34
machine learning/tensorflow/classification
convolutional neural networks/pre-trained models/vgg16
Contents
Files
Model weights -vgg16_weights.npz
TensorFlow model -vgg16.py
Class names -imagenet_classes.py
Example input -laska.png
To test run it, download all files to the same folder and run
python vgg16.py
Introduction
VGG is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes.
In this short post we provide an implementation of VGG16 and the weights from the original Caffe modelconverted to TensorFlow.
IMAGE CLASSIFICATION TASK
Architecture
The macroarchitecture of VGG16 can be seen in Fig. 2. We code it in TensorFlow in filevgg16.py. Notice that we include a preprocessing layer that takes the RGB image with pixels values in the range of 0-255 and subtracts the mean image values (calculated over the entire ImageNet training set).
MACROARCHITECTURE OF VGG16
Weights
We convert the Caffe weights publicly available in the author’sGitHub profileusing aspecialized tool. Some post-processing is done to ensure the model is in agreement with the TensorFlow standards. Finally, we arrive at the weights available invgg16_weights.npz.
Class Names
In order to associate the outputs of the model to class names we have the mapping available inimagenet_classes.py.
Future Content
We encourage you to get familiar with this model since it is widely used and will be the baseline for future content on knowledge transfer, guided backpropagation and other interesting topics on convolutional neural networks.
