Christian Beckmann chricke

## install_nginx_on_debian.sh
#!/bin/bash
#
# Install nginx on Debian.
#
# Author : Scott Barr
# Date   : 7 Jan 2010
#

VERSION=0.7.64


## .gitignore
.project
.pydevproject
.tmp*
*.pyc
*.db
.settings
.gitignore
*$
*.BAK
*.Z

## nginx
#!/bin/sh
### BEGIN INIT INFO
# Provides:          nginx
# Required-Start:    $remote_fs $syslog
# Required-Stop:     $remote_fs $syslog
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: nginx init.d dash script for Ubuntu <=9.10.
# Description:       nginx init.d dash script for Ubuntu <=9.10.
### END INIT INFO

## preprocess.py
import os
import cv2
import pandas as pd

root_dir = os.getcwd()
file_list = ['train.csv', 'val.csv']
image_source_dir = os.path.join(root_dir, 'data/images/')
data_root = os.path.join(root_dir, 'data')
for file in file_list:


## create_records.sh
lumi dataset transform --type csv --data-dir data/ --output-dir tfdata/ --split train --split val --only-classes=table

## config.yml
train:
  # Name used to identify the run. Data inside `job_dir` will be stored under
  # `run_name`.
  run_name: table-area-detection-0.1
  # Base directory in which model checkpoints & summaries (for Tensorboard) will
  # be saved.
  job_dir: jobs/
  save_checkpoint_secs: 10
  save_summaries_secs: 10

## start_training.sh
lumi train -c config.yml

## create_checkpoint.sh
lumi checkpoint create config.yml

## gist:80d9cd88c5293619c0be0a825920900e
lumi predict --checkpoint c2155084dca6 data/val/9541_023.png

## start_webbserver.sh
lumi server web --checkpoint c2155084dca6
	#!/bin/bash
	#
	# Install nginx on Debian.
	#
	# Author : Scott Barr
	# Date : 7 Jan 2010
	#

	VERSION=0.7.64
	.project
	.pydevproject
	.tmp*
	*.pyc
	*.db
	.settings
	.gitignore
	*$
	*.BAK
	*.Z
	#!/bin/sh
	### BEGIN INIT INFO
	# Provides: nginx
	# Required-Start: $remote_fs $syslog
	# Required-Stop: $remote_fs $syslog
	# Default-Start: 2 3 4 5
	# Default-Stop: 0 1 6
	# Short-Description: nginx init.d dash script for Ubuntu <=9.10.
	# Description: nginx init.d dash script for Ubuntu <=9.10.
	### END INIT INFO
	import os
	import cv2
	import pandas as pd

	root_dir = os.getcwd()
	file_list = ['train.csv', 'val.csv']
	image_source_dir = os.path.join(root_dir, 'data/images/')
	data_root = os.path.join(root_dir, 'data')
	for file in file_list:
	train:
	# Name used to identify the run. Data inside `job_dir` will be stored under
	# `run_name`.
	run_name: table-area-detection-0.1
	# Base directory in which model checkpoints & summaries (for Tensorboard) will
	# be saved.
	job_dir: jobs/
	save_checkpoint_secs: 10
	save_summaries_secs: 10