iamsdas

from typing import List
from PIL import Image


def convertDataToBinary(data: str) -> List[str]:
    binaryDataList = []
    for char in data:
        binaryDataList.append(format(ord(char), '08b'))
    return binaryDataList

iamsdas

Openbox
Tint2
Urxvt
lxappearance
breeze dark

for system-wide mouse configuration, one can edit /usr/share/icons/default/index.theme

tint2conf is the customizer
sudo xbps-query -Rs rxvt

iamsdas

{
  "citation": "@InProceedings{parkhi12a,\n  author       = \"Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.\",\n  title        = \"Cats and Dogs\",\n  booktitle    = \"IEEE Conference on Computer Vision and Pattern Recognition\",\n  year         = \"2012\",\n}",
  "description": "The Oxford-IIIT pet dataset is a 37 category pet image dataset with roughly 200\nimages for each class. The images have large variations in scale, pose and\nlighting. All images have an associated ground truth annotation of breed.",
  "downloadSize": "811092049",
  "location": {
    "urls": [
      "http://www.robots.ox.ac.uk/~vgg/data/pets/"
    ]
  },
  "name": "oxford_iiit_pet",

iamsdas

{
  "citation": "@ONLINE {Free Spoken Digit Dataset,\n    author = \"Zohar Jackson\",\n    title  = \"Spoken_Digit\",\n    year   = \"2016\",\n    url    = \"https://github.com/Jakobovski/free-spoken-digit-dataset\"\n}",
  "description": "A free audio dataset of spoken digits. Think MNIST for audio.\n\nA simple audio/speech dataset consisting of recordings of spoken digits in wav files at 8kHz.\nThe recordings are trimmed so that they have near minimal silence at the beginnings and ends.\n\n5 speakers\n2,500 recordings (50 of each digit per speaker)\nEnglish pronunciations\n\nFiles are named in the following format: {digitLabel}_{speakerName}_{index}.wav",
  "downloadSize": "11975353",
  "location": {
    "urls": [
      "https://github.com/Jakobovski/free-spoken-digit-dataset"
    ]
  },
  "name": "spoken_digit",

iamsdas

{
  "citation": "@InProceedings{Nilsback08,\n   author = \"Nilsback, M-E. and Zisserman, A.\",\n   title = \"Automated Flower Classification over a Large Number of Classes\",\n   booktitle = \"Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing\",\n   year = \"2008\",\n   month = \"Dec\"\n}",
  "description": "The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring\nin the United Kingdom. Each class consists of between 40 and 258 images. The images have\nlarge scale, pose and light variations. In addition, there are categories that have large\nvariations within the category and several very similar categories.\n\nThe dataset is divided into a training set, a validation set and a test set.\nThe training set and validation set each consist of 10 images per class (totalling 1020 images each).\nThe test set consists of the remaining 6149 images (minimum 20 per class).",
  "downloadSize": "344878000",
  "location": {
    "urls": [
      "https:/