twmht

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

twmht

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = git@github.com:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

twmht

Deep Learning for Face Recognition (May 2016)

Popular architectures

• FaceNet (Google)
  • They use a triplet loss with the goal of keeping the L2 intra-class distances low and inter-class distances high
• DeepID (Hong Kong University)
  • They use verification and identification signals to train the network. Afer each convolutional layer there is an identity layer connected to the supervisory signals in order to train each layer closely (on top of normal backprop)
• DeepFace (Facebook)
  • Convs followed by locally connected, followed by fully connected

twmht

#!/usr/bin/env python2
# coding: utf-8

import os,socket,threading,time
#import traceback

allow_delete = False
local_ip = socket.gethostbyname(socket.gethostname())
local_port = 8888
currdir=os.path.abspath('.')