Photos | Metropolis Parade
A crowd gathers under the city lights, surrounded by street signs and banners, as 36 people march by with flags and signs in hand. Among them are Sneha Deepthi and two men wearing hats and accessories. The urban landscape is marked by three traffic lights, two glass structures, and an office building in the background.
BLIP-2 Description:
a large crowd of peopleMetadata
Capture date:
Original Dimensions:
2336w x 3504h - (download 4k)
Usage
Dominant Color:
advertisement available urban flag parying street instante glasses transportation 个 ncent de one wai da st way antonio snack city dtazonds injusticer shop building sneha deepthi sign dinero les neighborhood 心 bar fight office los parade credit orate terms metropolis road vehicle symbol architecture dj hat text light angeles traffic banner ec pawn accessories dreai crowd casa
Detected Text
flash fired
true
iso
100
metering mode
5
aperture
f/1.2
focal length
85mm
shutter speed
1/8000s
camera make
Canon
camera model
lens model
overall
(36.11%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.55%)
failure
(-0.20%)
harmonious color
(-0.57%)
immersiveness
(0.46%)
interaction
(1.00%)
interesting subject
(-40.43%)
intrusive object presence
(-12.60%)
lively color
(-3.79%)
low light
(0.76%)
noise
(-1.34%)
pleasant camera tilt
(-10.59%)
pleasant composition
(-83.50%)
pleasant lighting
(-12.26%)
pleasant pattern
(6.71%)
pleasant perspective
(9.38%)
pleasant post processing
(5.99%)
pleasant reflection
(-1.64%)
pleasant symmetry
(0.85%)
sharply focused subject
(0.32%)
tastefully blurred
(-15.15%)
well chosen subject
(17.32%)
well framed subject
(-50.59%)
well timed shot
(2.40%)
all
(-1.92%)
* 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.