Photos | The Metropolis Parade
A large crowd of 53 people gathered in the urban streets, with colorful banners and signs shining under the bright light; Natasha Jonas, Neetu David, Rasanara Parwin, and Kotozakura Masakatsu are among the 8 adults and 7 males captured in the photo.
BLIP-2 Description:
a large crowd of peopleMetadata
Capture date:
Original Dimensions:
2336w x 3504h - (download 4k)
Usage
Dominant Color:
advertisement urban flag street glasses 个 outdoor amexican de one 山区 criminalsa footwear bag masakatsu agains natasha jonas abi da park st way xing ng pedestrian antonio city 及 shop il villa sign wth shoe avail 工 pafyizg handbag weare neighborhood fight g los ance parade credit terms wtsa metropolis road dream ped rasanara parwin symbol im workers text light traffic neetu neare banner kotozakura buy accessories david think crowd casa land
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
(27.05%)
curation
(50.00%)
highlight visibility
(4.36%)
behavioral
(70.62%)
failure
(-0.24%)
harmonious color
(-1.91%)
immersiveness
(0.51%)
interaction
(1.00%)
interesting subject
(-61.72%)
intrusive object presence
(-3.49%)
lively color
(4.19%)
low light
(1.00%)
noise
(-0.83%)
pleasant camera tilt
(-11.25%)
pleasant composition
(-84.96%)
pleasant lighting
(-21.84%)
pleasant pattern
(6.69%)
pleasant perspective
(-0.26%)
pleasant post processing
(4.79%)
pleasant reflection
(-2.84%)
pleasant symmetry
(0.54%)
sharply focused subject
(0.15%)
tastefully blurred
(-24.22%)
well chosen subject
(18.04%)
well framed subject
(-70.61%)
well timed shot
(-1.93%)
all
(-4.56%)
* 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.