alexdong

import Accelerate
import Dispatch
import Foundation
import os

let logger = Logger(subsystem: "photos.keepers", category: "EmbeddingVectorSearchEngine")

typealias Embedding = [Float32]
typealias Distance = Float32

alexdong

Quick summary of all the datasets contained in torchvision.

• Caltech101: Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc 'Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels. We have carefully clicked outlines of each object in these pictures, these are included under the 'Annotations.tar'. There is also a matlab script to view the annotaitons, 'show_annotations.m'.
• Caltech256: Collection of all 30607 images: 256_ObjectCategories.tar
• CelebA: CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations. The dataset can be employed as the training and test sets for the following computer vision tasks: face

alexdong

abacus
accordion
acorn
acrobat
adult
adult_cat
agriculture
aircraft
airplane
airport

alexdong

Verifying that +alexdong is my Bitcoin username. You can send me #bitcoin here: https://onename.io/alexdong

alexdong

/*
 
To use this script, replace the {{YOUR_TORPIO_KEY}} with your torpio.io API key 
and replace {{{{YOUR_CHOIR_IO_KEY}} with your choir.io key when you create an API source.

*/
 


var settings = {};

alexdong

var pick_color = (function(str) {
    // We use a circular buffer here to allocate the colors.  `label_color_map`
    // is a hash with key as `str` and value is the index into the `colors`
    // list. The following 'static' variables keep the state within the function.
    var label_color_map = {},
        current_color_idx = 0,
        colors = ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99',
        '#e31a1c', '#fdbf6f', '#ff7f00', '#cab2d6', '#6a3d9a', '#ffff99', '#b15928'];

    return function(str) {

alexdong

#!/bin/sh

files=$(git diff --cached --name-only --diff-filter=ACM | grep ".js$")
if [ "$files" = "" ]; then 
    exit 0 
fi

pass=true

for file in ${files}; do

alexdong

" copy all this into a vim buffer, save it, then...
" source the file by typing :so %
" Now the vim buffer acts like a specialized application for mastering vim

" There are two queues, Study and Known.  Depending how confident you feel
" about the item you are currently learning, you can move it down several
" positions, all the way to the end of the Study queue, or to the Known
" queue.

" type ,, (that's comma comma)

alexdong

#!/bin/sh

echo "Decompile into smali format ..."
apktool b ${1} /tmp/${1}-pack.apk

echo "\n\nSign the package. Password is mykey ..."
jarsigner -sigalg SHA1withRSA -digestalg SHA1 -keystore mykey.keystore /tmp/${1}-pack.apk mykey

echo "\n\nZip align the apk ..."
zipalign -f -v 4 /tmp/${1}-pack.apk /tmp/${1}.apk

alexdong

import random
import threading

class ThreadPool(object):

    def __init__(self, pool_size):
        self.pool_size = pool_size
        self.threads = []

    def wait_for_thread(self):