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Patriciasr92 / activity_main.xml
Created August 19, 2016 10:02 — forked from anonymous/activity_main.xml
RelativeLayout XML for Udacity quiz question
<RelativeLayout
xmlns:android="http://schemas.android.com/apk/res/android"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:padding="16dp">
<TextView
android:text="I’m in this corner"
android:layout_height="wrap_content"
android:layout_width="wrap_content"
@Patriciasr92
Patriciasr92 / FMP.md
Created September 25, 2016 11:26 — forked from shagunsodhani/FMP.md
Notes on Fractional Max-Pooling

Fractional Max-Pooling (FMP)

Introduction

  • Link to Paper
  • Spatial pooling layers are building blocks for Convolutional Neural Networks (CNNs).
  • Input to pooling operation is a Nin x Nin matrix and output is a smaller matrix Nout x Nout.
  • Pooling operation divides Nin x Nin square into N2out pooling regions Pi, j.
  • Pi, j ⊂ {1, 2, . . . , Nin} ∀ (i, j) ∈ {1, . . . , Nout}2
@Patriciasr92
Patriciasr92 / demo.py
Created September 30, 2016 10:23 — forked from twmht/demo.py
VGG Deep Face in python
import caffe
import numpy as np
# http://stackoverflow.com/questions/33828582/vgg-face-descriptor-in-python-with-caffe
img = caffe.io.load_image( "ak.png" )
img = img[:,:,::-1]*255.0 # convert RGB->BGR
avg = np.array([129.1863,104.7624,93.5940])
img = img - avg # subtract mean (numpy takes care of dimensions :)
img = img.transpose((2,0,1))
img = img[None,:] # add singleton dimension
net = caffe.Net("VGG_FACE_deploy.prototxt","VGG_FACE.caffemodel", caffe.TEST)
@Patriciasr92
Patriciasr92 / readme.md
Created September 30, 2016 10:25 — forked from baraldilorenzo/readme.md
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

name: "VGG_FACE_16_layer"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layer {
name: "data"
type: "Data"
top: "data"
@Patriciasr92
Patriciasr92 / readme.md
Created October 4, 2016 09:06 — forked from baraldilorenzo/readme.md
VGG-19 pre-trained model for Keras

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

@Patriciasr92
Patriciasr92 / keras VGG-Face Model.md
Created November 3, 2016 15:14 — forked from EncodeTS/keras VGG-Face Model.md
VGG-Face model for keras

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

  • vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
  • vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper:

@Patriciasr92
Patriciasr92 / readme.md
Created December 25, 2016 20:43 — forked from nitish11/readme.md
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

@Patriciasr92
Patriciasr92 / ubuntu.sh
Created January 8, 2018 08:52 — forked from jarutis/ubuntu.sh
Theano and Keras setup on ubuntu with OpenCL on AMD card
## install Catalyst proprietary
sudo ntfsfix /dev/sda2
sudo cp /etc/X11/xorg.conf /etc/X11/xorg.conf.BAK
sudo apt-get remove --purge fglrx*
sudo apt-get install linux-headers-generic
sudo apt-get install fglrx xvba-va-driver libva-glx1 libva-egl1 vainfo
sudo amdconfig --initial
## install build essentials
sudo apt-get install cmake