Photos | The Architectural Marvels of Chicago's Edge Race Map
This intricate map showcases the campus buildings, neighborhood intersections, and outdoor parks that make up the exciting route of the Chicago Edge Race 2019.
BLIP-2 Description:
the map for the chicago edge raceMetadata
Capture date:
Original Dimensions:
900w x 530h - (download 4k)
Usage
Dominant Color:
advertisement urban box plan festival exit lindley park food + scene document airport clean nature leave vip field sponsors vendors 国 keep station venue en found uber poster main hellman request hollow building ride campus outdoors diagram lost san plant marx bike viewing emergency colden land plot drinks south 肉肉 toilets grounds grass pass meadow sutro 內 煎 ave general paths 限 neighborhood twin aug perks architecture cate refill mclaren francisco 雪 map chart walking water info merchandise parking no city medical lands intersection end entry trace gate office area road ca polo terminal accessibility booth entrance
Detected Text
11 2019 25th 27th 30ch 68 9 aug ave accessibility between booth bike box ca cate colden drinks entrance exit emergency entry festival food francisco gate general grounds hellman hollow info keep lands leave lindley lost main marx mclaren meadow merchandise parking pass perks polo paths refill scene sutro san south sponsors trace twin toilets uber venue viewing vip vendors walking area clean en end field found medical no office on request ride s station the water with 內 国 煎 肉肉 限 雪 +
curation
(25.00%)
highlight visibility
(1.98%)
behavioral
(10.02%)
failure
(-0.12%)
harmonious color
(3.58%)
immersiveness
(0.07%)
interaction
(1.00%)
interesting subject
(-81.54%)
intrusive object presence
(-3.42%)
lively color
(13.06%)
low light
(0.07%)
noise
(-1.17%)
pleasant camera tilt
(-6.34%)
pleasant composition
(-22.06%)
pleasant lighting
(15.04%)
pleasant pattern
(3.78%)
pleasant perspective
(13.51%)
pleasant post processing
(-21.72%)
pleasant reflection
(-0.02%)
pleasant symmetry
(0.73%)
sharply focused subject
(6.67%)
tastefully blurred
(-6.85%)
well chosen subject
(0.66%)
well framed subject
(23.08%)
well timed shot
(4.34%)
all
(1.12%)
* NOTE: This image was scaled up from its original size using an AI model called GFP-GAN (Generative Facial Prior), which is a
Generative adversartial network that can be used to repair (or upscale in this case) photos, sometimes the results are a little...
weird.
* 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.