This repo is a quick and dirty matconvnet speed comparison of the standard vgg-vd-16 model and its atrous counterpart (originally described in the Parsenet paper). The atrous model was imported into matconvnet from its caffe version.
###Results
GPU tests performed with a single Tesla M40 (should be considered rough approximations)
| Model | CPU | GPU |
|---|---|---|
| standard vgg-vd-16 | 1.5Hz | 44.5Hz |
| atrous vgg-vd-16 | 1.7Hz | 48.2Hz |
###NOTES The original model was designed for 1000-way image classification, whereas the Atrous model is designed to be used as the trunk in an object detection framework (for instance, performing 21-way classification on Pascal VOC data).
The primary difference between the models is that the fully connected fc6
layer in the original is exchanged for a dilated convolution layer. The
following layers are also fully convolutional, which dramatically reduces
the number of parameters in the network (as can be seen in the table below)
###Memory Requirements
| Model | Memory (params) | Memory (vars) |
|---|---|---|
| Standard vgg-vd-16 | 528MB | 1.097GB |
| Atrous vgg-vd-16 | 82MB | 1.109GB |
The second column lists the memory required to hold the variables used by the
network to process a batch of ten RGB images each of dimension 224 x 224 x 3
stored in single precision.