From 898e6cdf8df0993f853b748d4e8a9c269fad0294 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sat, 15 Dec 2018 22:14:17 +0100 Subject: inject applet payload --- site/public/datasets/lfw/index.html | 51 ++++++++++++------------------- site/public/datasets/vgg_face2/index.html | 9 ++---- 2 files changed, 21 insertions(+), 39 deletions(-) (limited to 'site/public/datasets') diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index e080229f..3c83acd3 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -28,12 +28,7 @@

Labeled Faces in the Wild

Created
2007
Images
13,233
People
5,749
Created From
Yahoo News images
Search available
Searchable

Labeled Faces in The Wild (LFW) is amongst the most widely used facial recognition training datasets in the world and is the first of its kind to be created entirely from images posted online. The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. Use the tools below to check if you were included in this dataset or scroll down to read the analysis.

-

{INSERT IMAGE SEARCH MODULE}

-

{INSERT TEXT SEARCH MODULE}

-
load file: lfw_names_gender_kg_min.csv
-Name, Images, Gender, Description
-
-
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.

Intro

+
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.

Intro

Three paragraphs describing the LFW dataset in a format that can be easily replicated for the other datasets. Nothing too custom. An analysis of the initial research papers with context relative to all the other dataset papers.

all 5,749 people in the LFW Dataset sorted from most to least images collected.
all 5,749 people in the LFW Dataset sorted from most to least images collected.

LFW by the Numbers

import numpy as np from sklearn.datasets import fetch_lfw_people import imageio -import imutils - -# download LFW dataset (first run takes a while) -lfw_people = fetch_lfw_people(min_faces_per_person=1, resize=1, color=True, funneled=False) - -# introspect dataset -n_samples, h, w, c = lfw_people.images.shape -print('{:,} images at {}x{}'.format(n_samples, w, h)) +import imutils

+

download LFW dataset (first run takes a while)

+

lfw_people = fetch_lfw_people(min_faces_per_person=1, resize=1, color=True, funneled=False)

+

introspect dataset

+

n_samples, h, w, c = lfw_people.images.shape +print('{:,} images at {}x{}'.format(n_samples, w, h)) cols, rows = (176, 76) -n_ims = cols * rows - -# build montages -im_scale = 0.5 +n_ims = cols * rows

+

build montages

+

im_scale = 0.5 ims = lfw_people.images[:n_ims -montages = imutils.build_montages(ims, (int(w*im_scale, int(h*im_scale)), (cols, rows)) -montage = montages[0] - -# save full montage image -imageio.imwrite('lfw_montage_full.png', montage) - -# make a smaller version -montage_960 = imutils.resize(montage, width=960) -imageio.imwrite('lfw_montage_960.jpg', montage_960) - -

Disclaimer

+montages = imutils.build_montages(ims, (int(wim_scale, int(him_scale)), (cols, rows)) +montage = montages[0]

+

save full montage image

+

imageio.imwrite('lfw_montage_full.png', montage)

+

make a smaller version

+

montage_960 = imutils.resize(montage, width=960) +imageio.imwrite('lfw_montage_960.jpg', montage_960)

+

Disclaimer

MegaPixels is an educational art project designed to encourage discourse about facial recognition datasets. Any ethical or legal issues should be directed to the researcher's parent organizations. Except where necessary for contact or clarity, the names of researchers have been subsituted by their parent organization. In no way does this project aim to villify researchers who produced the datasets.

Read more about MegaPixels Code of Conduct

diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index 24a1059b..817fc9a0 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -28,12 +28,7 @@

VGG Faces2

Created
2018
Images
3.3M
People
9,000
Created From
Scraping search engines
Search available
[Searchable](#)

VGG Face2 is the updated version of the VGG Face dataset and now includes over 3.3M face images from over 9K people. The identities were selected by taking the top 500K identities in Google's Knowledge Graph of celebrities and then selecting only the names that yielded enough training images. The dataset was created in the UK but funded by Office of Director of National Intelligence in the United States.

-

{INSERT IMAGE SEARCH MODULE}

-

{INSERT TEXT SEARCH MODULE}

-
load file: lfw_names_gender_kg_min.csv
-Name, Images, Gender, Description
-
-

VGG Face2 by the Numbers

+

VGG Face2 by the Numbers

  • 1,331 actresses, 139 presidents
  • 3 husbands and 16 wives
    • @@ -47,7 +42,7 @@ Name, Images, Gender, Description
  • The original VGGF2 name list has been updated with the results returned from Google Knowledge
  • Names with a similarity score greater than 0.75 where automatically updated. Scores computed using import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()
  • The 97 names with a score of 0.75 or lower were manually reviewed and includes name changes validating using Wikipedia.org results for names such as "Bruce Jenner" to "Caitlyn Jenner", spousal last-name changes, and discretionary changes to improve search results such as combining nicknames with full name when appropriate, for example changing "Aleksandar Petrović" to "Aleksandar 'Aco' Petrović" and minor changes such as "Mohammad Ali" to "Muhammad Ali"
    • -
  • The 'Description` text was automatically added when the Knowledge Graph score was greater than 250
    • +
  • The 'Description' text was automatically added when the Knowledge Graph score was greater than 250

TODO

    -- cgit v1.2.3-70-g09d2