lucasb-eyer

TsvHttpData-1.0
https://image-net.org/data/winter21_whole.tar.gz	1185381173159	qzE84DF5/YA6QBsCxlHAog==

lucasb-eyer

# Commands can be sent in lists of arbitrary length, allowing for arbitrarily complex instructions per frame. The user must explicitly request output data:
from tdw.controller import Controller
from tdw.tdw_utils import TDWUtils
from tdw.librarian import ModelLibrarian
from tdw.output_data import OutputData, Bounds, Images

c = Controller(launch_build=True)

lib = ModelLibrarian("models_core.json")

lucasb-eyer

from collections import defaultdict
import numpy as np
from scipy.optimize import linear_sum_assignment
from scipy.spatial.distance import cdist
from sklearn.metrics import auc

def prec_rec_2d(det_scores, det_coords, det_frames, gt_coords, gt_frames, gt_radii):
    """ Computes full precision-recall curves at all possible thresholds.

    Arguments:

lucasb-eyer

# count number of posters
wget -O - http://www.eccv2016.org/main-conference/ | grep -i 'pdf_icon' | wc -l
# == 504

# number of deep-learning ones
wget -O - http://www.eccv2016.org/main-conference/ | grep -i 'deep\|convolutional\|network\|cnn\|neural' | wc -l
# == 127

# Note the numbers are approximates because some posters are present twice, there's a legend on the icons, etc.

lucasb-eyer

class Dummy():
    def __init__(self, *a, **kw):
        pass

    def __setstate__(self, *a, **kw):
        pass

    def __getstate__(self, *a, **kw):
        pass

lucasb-eyer

{
"n02112350": 148,
"n04344873": 311,
"n01692333": 470,
"n03459775": 725,
"n04133789": 751,
"n01871265": 214,
"n04366367": 681,
"n03891332": 527,
"n03085013": 543,

lucasb-eyer

-- Create a battery widget on laptops
mybattery = nil
mybattery_timer = nil
if lucasb.file_exists("/sys/class/power_supply/BAT0/status") then
    mybattery = awful.widget.progressbar()
    mybattery:set_width(13)
    mybattery:set_vertical(true)
    mybattery_tip = awful.tooltip({objects = {mybattery}})
    lastalarm = 0

lucasb-eyer

#include <wand/magick_wand.h>
#include <iostream>
#include <vector>

using namespace std;

int main(int argc, char* argv[])
{
    MagickWand *mw_in = NewMagickWand();
    MagickWand *mw_out = NewMagickWand();

lucasb-eyer

Fitted linear SVM
 * Non-zero weights: 100
 * Accuracy: 0.963000
 * Iterations: 100
 * Converged: true

Computing Raw scores
--------------------
vectorized julia
elapsed time: 0.641106641 seconds (80048000 bytes allocated)

lucasb-eyer

julia> function f(x)                                           
         for i=1:length(x)                        
           @inbounds x[i] *= 2   
         end
       end
f (generic function with 1 method)

julia> code_llvm(f, (Matrix{Float64},))

define internal void @julia_f(%jl_value_t*) {