Photos | Student Protest in Metropolis
Anirudh Thapa joins a crowd of 43 people protesting in front of a skyscraper in the urban city, holding signs and waving 6 flags. The banner reads Stop Corporate Greed while lampposts and traffic lights loom in the background.
BLIP-2 Description:
a large group of people holding signs and flags in front of a buildingChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
3504w x 2336h - (download 4k)
Usage
Dominant Color:
architecture activities flag urban broadway leisure building parents student transportation hat road outdoor text performance traffic city bag art anirudh thapa gobi parade no glasses metropolis banner sign sac car vehicle light workers accessories skyscraper adventure office recreation bush handbag lamppost borders protest crowd symbol
iso
100
metering mode
5
aperture
f/4
focal length
22mm
shutter speed
1/500s
camera make
Canon
camera model
lens model
date
2006-04-15T10:38:51-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(39.60%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.62%)
failure
(-0.12%)
harmonious color
(-0.70%)
immersiveness
(0.59%)
interaction
(1.00%)
interesting subject
(-46.58%)
intrusive object presence
(-9.81%)
lively color
(6.01%)
low light
(1.29%)
noise
(-0.73%)
pleasant camera tilt
(-6.41%)
pleasant composition
(-81.69%)
pleasant lighting
(-3.74%)
pleasant pattern
(8.98%)
pleasant perspective
(15.10%)
pleasant post processing
(1.22%)
pleasant reflection
(1.76%)
pleasant symmetry
(0.42%)
sharply focused subject
(0.29%)
tastefully blurred
(-0.03%)
well chosen subject
(5.90%)
well framed subject
(-57.08%)
well timed shot
(3.92%)
all
(-0.16%)
* 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.