Photos | Crowd of People Catching the Sunset in Santa Clara

Subbaraman Vijayalakshmi and Naoya Ogawa join 29 other people in watching the sunset while walking down the street in Santa Clara, California.
BLIP-2 Description:
a crowd of people walking down the street at sunsetMetadata
Capture date:
Original Dimensions:
3024w x 4032h - (download 4k)
Usage
Dominant Color:
Location:
lighting jacket cap faithful outdoors urban city beanie gate glove car vehicle sky teen ogawa transportation hoodie girl accessories flare helmet photography baseball shirt naoya outdoor street toyota sun road glasses building footwear sunrise crowd architecture arena concert audience portrait light no metropolis coat sunlight subbaraman vijayalakshmi headgear sunset hat fence flag nature stadium shoe
iso
32
metering mode
5
aperture
f/1.8
focal length
4mm
latitude
37.41
longitude
-121.97
shutter speed
1/1136s
camera make
Apple
camera model
date
2020-01-11T16:38:22.477000-08:00
tzoffset
-28800
tzname
GMT-0800
overall
(40.11%)
curation
(50.00%)
highlight visibility
(4.51%)
behavioral
(90.73%)
failure
(-0.56%)
harmonious color
(3.73%)
immersiveness
(0.93%)
interaction
(1.00%)
interesting subject
(-27.98%)
intrusive object presence
(-5.69%)
lively color
(3.25%)
low light
(28.71%)
noise
(-2.91%)
pleasant camera tilt
(-11.99%)
pleasant composition
(-55.76%)
pleasant lighting
(-30.83%)
pleasant pattern
(12.43%)
pleasant perspective
(-11.68%)
pleasant post processing
(5.21%)
pleasant reflection
(-3.30%)
pleasant symmetry
(0.27%)
sharply focused subject
(0.49%)
tastefully blurred
(1.79%)
well chosen subject
(-23.72%)
well framed subject
(-42.26%)
well timed shot
(-3.13%)
all
(-3.94%)
* 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.