Tom Runia tomrunia

## icf_detector.json
{
	"icfdetector" :
	{
		"boost_type" : "WaldBoost",
		"detection_max_size" : 200,
		"detection_min_size" : 30,
		"feature_candidates" : 40000,
		"weak_count" : 600,
		"min_feature_size" : 25,
		"model_height" : 64,

## tensorflow_log_loader.py
import numpy as np
from tensorflow.python.summary.event_accumulator import EventAccumulator

import matplotlib as mpl
import matplotlib.pyplot as plt

def plot_tensorflow_log(path):

    # Loading too much data is slow...
    tf_size_guidance = {

## README.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                tomrunia
                / README.md
            
            
              Created
              March 3, 2016 15:23
            
              
                Caffe model benchmarks: average classification time per image on MS-COCO (GPU Titan X)
              
          
    Caffe Classification

Classification Speed Benchmark

Table lists average classification times per image (milliseconds). Averages are
computed over classification of 100 images from MS-COCO validation set. For the VGG networks
a very high variance in classification times was observed, some images were classified fast
while most of them took more processing time that other network configurations.

  
## cat-classification-caffe.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                tomrunia
                / cat-classification-caffe.md
            
            
              Created
              March 14, 2016 09:18
            
          
    AlexNet

Classification done in 79.1ms [avg = 79.1ms]
Probabilities and labels:
n02124075 Egyptian cat (0.31)
n02123159 tiger cat (0.18)
n02123045 tabby, tabby cat (0.12)
n02119022 red fox, Vulpes vulpes (0.11)
n02085620 Chihuahua (0.04)

  
## youtube_search_and_download.py
#!/usr/bin/python
import os

# YouTube video searching API
from apiclient.discovery import build
from apiclient.errors import HttpError
from oauth2client.tools import argparser

# Downloading YouTube videos
import pafy

## Chrome.sublime-build
{
	"cmd": ["open", "-a", "Google Chrome", "$file", "--args", "--allow-file-access-from-files"]
}

## barchart.html
<!DOCTYPE html>
<meta charset="utf-8">

<!-- The visual styling of our barchart is in this file -->
<link rel="stylesheet" href="barchart.css">

<!-- This is the main container to which we append all chart elements -->
<svg class="chart"></svg>

<!-- We include D3 from an external location so that we don't have

## test_queue.py
import time
import numpy as np
import threading

import tensorflow as tf
from tensorflow.python.client import timeline


def test_queue():

## tf_summary_reader.py
import os
import sys
import pickle
import numpy as np
import tensorflow as tf
from tensorflow.python.summary.event_accumulator import EventAccumulator


event_file = "/home/trunia1/dev/python/LSTMCounting/output/screens/" \
             "summaries/004_rmsprop_0.0005_lstm_512x2_grad_10_batch_128_dropkp_0.75/" \

## tf_queue.py


# Initialize placeholders for feeding in to the queue
pl_queue_screens = tf.placeholder(tf.float32, shape=[config.seq_length, config.image_size, config.image_size, config.input_channels], name="queue_inputs")
pl_queue_targets = tf.placeholder(tf.uint8, shape=[config.seq_length], name="queue_targets_cnt")

#  ...

capacity = config.min_after_dequeue + 10 * (config.num_gpus*config.batch_size)
q = tf.RandomShuffleQueue(
	{
	"icfdetector" :
	{
	"boost_type" : "WaldBoost",
	"detection_max_size" : 200,
	"detection_min_size" : 30,
	"feature_candidates" : 40000,
	"weak_count" : 600,
	"min_feature_size" : 25,
	"model_height" : 64,
	import numpy as np
	from tensorflow.python.summary.event_accumulator import EventAccumulator

	import matplotlib as mpl
	import matplotlib.pyplot as plt

	def plot_tensorflow_log(path):

	# Loading too much data is slow...
	tf_size_guidance = {
	#!/usr/bin/python
	import os

	# YouTube video searching API
	from apiclient.discovery import build
	from apiclient.errors import HttpError
	from oauth2client.tools import argparser

	# Downloading YouTube videos
	import pafy
	{
	"cmd": ["open", "-a", "Google Chrome", "$file", "--args", "--allow-file-access-from-files"]
	}
	<!DOCTYPE html>
	<meta charset="utf-8">

	<!-- The visual styling of our barchart is in this file -->
	<link rel="stylesheet" href="barchart.css">

	<!-- This is the main container to which we append all chart elements -->
	<svg class="chart"></svg>

	<!-- We include D3 from an external location so that we don't have
	import time
	import numpy as np
	import threading

	import tensorflow as tf
	from tensorflow.python.client import timeline


	def test_queue():
	import os
	import sys
	import pickle
	import numpy as np
	import tensorflow as tf
	from tensorflow.python.summary.event_accumulator import EventAccumulator


	event_file = "/home/trunia1/dev/python/LSTMCounting/output/screens/" \
	"summaries/004_rmsprop_0.0005_lstm_512x2_grad_10_batch_128_dropkp_0.75/" \


	# Initialize placeholders for feeding in to the queue
	pl_queue_screens = tf.placeholder(tf.float32, shape=[config.seq_length, config.image_size, config.image_size, config.input_channels], name="queue_inputs")
	pl_queue_targets = tf.placeholder(tf.uint8, shape=[config.seq_length], name="queue_targets_cnt")

	# ...

	capacity = config.min_after_dequeue + 10 * (config.num_gpus*config.batch_size)
	q = tf.RandomShuffleQueue(