Photos | Women's March Draws Thousands to the Streets
Brandon Stanton captures the moment as a large crowd holds signs and takes to the streets in protest during the 2017 Women's March.
BLIP-2 Description:
a large crowd of people holding signs in the streetChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
5760w x 3840h - (download 4k)
Usage
Dominant Color:
soner men activities xing flag urban plant building tolence pre leisure brandon iii eos hat stanton tree outdoor house ped traffic light expect text performance sky city sporting cloudy womens art event fuck adoesfre blue parade glasses coachella audience existenge january winter metropolis mark rekognition_c banner sign light respect accessories adventure recreation flick canon entertainment lamppost palm protest concert silence crowd march resistance olem oppressed
Detected Text
iso
400
metering mode
5
aperture
f/2.8
focal length
16mm
shutter speed
1/8000s
camera make
Canon
camera model
lens model
date
2017-01-21T12:51:38.490000-08:00
tzoffset
-28800
tzname
America/Los_Angeles
overall
(33.98%)
curation
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highlight visibility
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behavioral
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failure
(-0.39%)
harmonious color
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immersiveness
(0.81%)
interaction
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interesting subject
(-64.40%)
intrusive object presence
(-17.72%)
lively color
(1.91%)
low light
(9.67%)
noise
(-1.76%)
pleasant camera tilt
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pleasant composition
(-83.54%)
pleasant lighting
(-13.00%)
pleasant pattern
(5.52%)
pleasant perspective
(1.94%)
pleasant post processing
(-0.19%)
pleasant reflection
(-2.63%)
pleasant symmetry
(0.37%)
sharply focused subject
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tastefully blurred
(-5.65%)
well chosen subject
(7.56%)
well framed subject
(-66.46%)
well timed shot
(-2.48%)
all
(-3.54%)
* NOTE: Amazon Rekognition
detected a celebrity in this image using the
Celebrity Recognition API. The API isn't perfect, but it does give you the MatchConfidence which I display
next to the celebrity's name along with links _↗ to their info.
* WARNING: The title and caption of this image were generated by an AI LLM (gpt-3.5-turbo-0301
from
OpenAI)
based on a
BLIP-2 image-to-text labeling, tags,
location,
people
and album metadata from the image and are
potentially inaccurate, often hilariously so. If you'd like me to adjust anything,
just reach out.