kingsj0405

"""
This program is wrapper of face_recognition on the following link
    https://github.com/ageitgey/face_recognition

You need to install followings
    - Python3.3+ or Python2.7
    - dlib
    - face_recognition

For more information visit ageithey/face_recognition

kingsj0405

#!/bin/sh
# From following link
# https://stackoverflow.com/a/29248777/6322404
for zip in *.zip
do
  dirname=`echo $zip | sed 's/\.zip$//'`
  if mkdir "$dirname"
  then
    if cd "$dirname"
    then

kingsj0405

# Forked from vgg perceptual loss
# https://gist.github.com/alper111/8233cdb0414b4cb5853f2f730ab95a49
import torch
import torchvision


class VGGFeatureExtractor(torch.nn.Module):
    def __init__(self, resize=True):
        super(VGGFeatureExtractor, self).__init__()
        blocks = []

kingsj0405

#!/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/

kingsj0405

Result

(venv) root@host:path/FFHQ# python convert_lmdb1024_list256.py
70000it [14:16, 81.68it/s]________________________________________________________________________________________________________________________________________________________________________________________________________________________________| 69997/70000 [14:16<00:00, 26.75it/s]
100%|_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________| 70000/70000 [14:16<00:00, 81.68it/s]

kingsj0405

The oxford-IIIT Pet Dataset

Homepage

Download and extraction

Run provided download.sh

kingsj0405

https://code.visualstudio.com/docs/datascience/jupyter-notebooks#_connect-to-a-remote-jupyter-server

그런데 그냥 위에걸 잘 쓰는게 더 나은듯

kingsj0405

// 신청인원 보고서
// 신청내역 나오는 화면에서 콘솔(F12) 열어서 

var data_my_mil;
var finished1 = false;
var finished2 = false;
function call_data(){
    call_data2();
    jQuery('table').fadeTo('fast',0.5);
    finished1 = false;

kingsj0405

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

kingsj0405

Including iframe of an github (gists) embled script

So for include a github embled iframe into an iframe You may use hex code

see this exemple :
The github embled link : <script src="https://gist.github.com/lostsh/dfb8a51aeb3ad5d7e3cefbc66d72fa53.js"></script> and ths iframe who contain this embled :