Photos | Unity in Diversity

A large crowd of 37 people, including Azharuddin Mohammed Ismail, come together in white shirts and holding signs to promote unity and diversity in the community. The parade showcases various clothing, accessories, and headgear while also highlighting the importance of art and recreation.
BLIP-2 Description:
a large group of people in white shirts and holding signsMetadata
Capture date:
Original Dimensions:
3504w x 2336h - (download 4k)
Usage
Dominant Color:
headgear boy necklace flag baseball ass aquí footwear child cap hat wristwatch dom gif text performance grag años todo shoe bag art nationa sab parade parking glasses estacionamiento banner sign jewelry accessories entre recreation handbag el protest free azharuddin mohammed ismail día novias crowd march
Detected Text
iso
100
metering mode
5
aperture
f/4
focal length
17mm
shutter speed
1/800s
camera make
Canon
camera model
lens model
date
2006-03-25T10:41:14-08:00
tzoffset
-28800
tzname
America/Los_Angeles
overall
(23.32%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.54%)
failure
(-0.51%)
harmonious color
(-4.99%)
immersiveness
(0.24%)
interaction
(1.00%)
interesting subject
(-74.90%)
intrusive object presence
(-15.94%)
lively color
(-4.12%)
low light
(31.37%)
noise
(-3.54%)
pleasant camera tilt
(-12.43%)
pleasant composition
(-94.87%)
pleasant lighting
(-35.74%)
pleasant pattern
(6.18%)
pleasant perspective
(-13.57%)
pleasant post processing
(2.68%)
pleasant reflection
(-1.46%)
pleasant symmetry
(0.15%)
sharply focused subject
(0.27%)
tastefully blurred
(-7.56%)
well chosen subject
(-0.80%)
well framed subject
(-79.79%)
well timed shot
(6.45%)
all
(-8.47%)
* 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.