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owahltinez /
Last active September 28, 2022 01:19
Processes a CSV file using AutoML's object detection format into tfrecords.
"""Processes a CSV file using AutoML's object detection format into tfrecords.
This script will accept a CSV file path or URL and write tfrecord files at the
provided output path. Example usage:
mkdir -p /tmp/salad_dataset
python \
--csv_path=gs://cloud-ml-data/img/openimage/csv/salads_ml_use.csv \
"embeddings": [
"tensorName": "My tensor",
"tensorShape": [
"tensorPath": "",
"metadataPath": ""
We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.
We can't make this file beautiful and searchable because it's too large.
-2.247926779091358185e-02 4.126220196485519409e-02 2.201553061604499817e-02 4.435282200574874878e-02 4.219446703791618347e-02 7.351647363975644112e-04 -1.420196741819381714e-01 3.519088402390480042e-02 7.742006331682205200e-02 1.293895300477743149e-02 7.603470981121063232e-02 -1.221615076065063477e-01 -2.842330746352672577e-02 -6.303963717073202133e-03 -3.483119606971740723e-02 2.308848127722740173e-02 8.119910210371017456e-02 1.293514519929885864e-01 1.212856918573379517e-01 -7.862001657485961914e-02 -1.989656686782836914e-01 -1.172609031200408936e-01 1.417047232389450073e-01 5.320393666625022888e-02 -4.523742944002151489e-02 -1.421208977699279785e-01 -1.612507738173007965e-02 -8.112639933824539185e-02 -1.910826005041599274e-02 1.615460962057113647e-01 -4.172783717513084412e-02 1.076077520847320557e-01 1.423008143901824951e-01 -9.967331588268280029e-02 -1.324560940265655518e-01 7.485345005989074707e-02 -1.712135504931211472e-03 -7.529520243406295776e-02 4.165450483560562134e-02 -3.049586899578571320e-02 -7.7
owahltinez /
Last active October 6, 2021 02:14
Recursively list all files in an Azure container using `az storage fs file list`
import json
import os
import sys
import time
def list_files(opts: str, marker: str = None) -> list:
fpath = f"/tmp/{int(1E9 * time.monotonic())}.json"
marker_opt = f'--marker "{marker}"' if marker else ""
os.system(f'az storage fs file list {opts} --show-next-marker {marker_opt} > "{fpath}"')
key city_wikidata city_name population
AD_02 Q24413 El Tarter 747
AE_AJ Q530171 Ajman 238119
AE_AZ Q1519 Abu Dhabi 1000000
AE_AZ Q234600 Al Ain 631005
AE_AZ Q234600 Al Ain 766936
AE_AZ Q12241857 Madinat Zayed 29095
AE_DU Q612 Dubai 2502715
AE_FU Q4045 Fujairah 93673
AE_RK Q2126436 Ras al-Khaimah 115949
owahltinez / brazil_municipalities.csv
Created July 17, 2020 18:59
Municipalities of Brazil
key country_code country_name subregion1_code subregion1_name subregion2_code subregion2_name locality_code locality_name match_string aggregate_report_offset
BR_AC_1200013 BR Brazil AC Acre 1200013 Acrelândia
BR_AC_1200054 BR Brazil AC Acre 1200054 Assis Brasil
BR_AC_1200104 BR Brazil AC Acre 1200104 Brasiléia
BR_AC_1200138 BR Brazil AC Acre 1200138 Bujari
BR_AC_1200179 BR Brazil AC Acre 1200179 Capixaba
BR_AC_1200203 BR Brazil AC Acre 1200203 Cruzeiro do Sul
BR_AC_1200252 BR Brazil AC Acre 1200252 Epitaciolândia
BR_AC_1200302 BR Brazil AC Acre 1200302 Feijó
BR_AC_1200328 BR Brazil AC Acre 1200328 Jordão
owahltinez / data_loading.ipynb
Created May 4, 2020 19:35
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// RECOMMENDED: Bind use cases to a lifecycle in a single call:
this as LifecycleOwner, preview, imageCapture, imageAnalyzer)
// Do *NOT* bind your use cases like this
CameraX.bindToLifecycle(this as LifecycleOwner, preview)
CameraX.bindToLifecycle(this as LifecycleOwner, imageCapture)
CameraX.bindToLifecycle(this as LifecycleOwner, imageAnalyzer)
// STEP 1: Define use case configuration
val imageCaptureConfig = ImageCaptureConfig.Builder()
.setTargetResolution(Size(1280, 720))
// STEP 2: Create the use case object
val imageCapture = ImageCapture(imageCaptureConfig)
// STEP 3: Bind the use case to our lifecycle
CameraX.bindToLifecycle(this as LifecycleOwner, imageCapture)