Photos | Rainbow Table of Carnival Fun

Bai Xue stopping by the colorful table of accessories, toys, and sweets at the Civic Center Mall festival in Los Angeles in 2016.
BLIP-2 Description:
a table with a rainbow and a bunch of items on itMetadata
Capture date:
Original Dimensions:
3024w x 4032h - (download 4k)
Usage
Dominant Color:
Location:
cream plush please october apple civic ora plant market soccer los angeles civic center mall toy downtown mammal bag accessories er little tokyo geri animal performance sport states arrangement deloved iphone park football grand ave food pet dog dessert bai xue continental concert kiosk little icing tokyo ball shrub fall oranc handbag united california plus canine carnival pinata hat flower fence center festival ca sweets entertainment
iso
20
metering mode
5
aperture
f/1.8
focal length
4mm
latitude
34.06
longitude
-118.25
shutter speed
1/451s
camera make
Apple
camera model
date
2016-10-29T16:25:26.552000-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(40.70%)
curation
(50.00%)
highlight visibility
(4.35%)
behavioral
(70.44%)
failure
(-0.24%)
harmonious color
(0.32%)
immersiveness
(0.22%)
interaction
(1.00%)
interesting subject
(-10.53%)
intrusive object presence
(-2.64%)
lively color
(25.27%)
low light
(25.24%)
noise
(-14.31%)
pleasant camera tilt
(-4.77%)
pleasant composition
(-17.31%)
pleasant lighting
(-4.13%)
pleasant pattern
(25.10%)
pleasant perspective
(2.31%)
pleasant post processing
(0.29%)
pleasant reflection
(-6.06%)
pleasant symmetry
(0.98%)
sharply focused subject
(2.05%)
tastefully blurred
(-3.32%)
well chosen subject
(-18.26%)
well framed subject
(18.75%)
well timed shot
(6.09%)
all
(3.99%)
* 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.