Photos | Gathering for Lunch at Local Cafeteria
Dave B and a group of seven men enjoy a meal and conversation at a bustling cafeteria in Los Angeles. The diverse group is seated at a communal dining table and surrounded by furniture and home decor, while plates and cutlery scatter the surface.
BLIP-2 Description:
a group of people at a table with foodChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
960w x 1280h - (download 4k)
Usage
Location:
rekognition_c todd couch justin m wood bracelet plate restaurant portrait andy n child glasses cutlery shirt dave sink footwear bag court buffet shorts food spoon cafeteria bowl chair jewelry table building lunch faucet linen shoe handbag decor pottery boy room pants dish cafe wristwatch cup architecture meal furniture district accessories photography dining indoors home
iso
125
metering mode
5
aperture
f/2.2
focal length
3mm
latitude
34.05
longitude
-118.26
shutter speed
1/30s
camera make
Apple
camera model
lens model
overall
(30.81%)
curation
(92.10%)
highlight visibility
(7.57%)
behavioral
(70.68%)
failure
(-0.81%)
harmonious color
(-0.17%)
immersiveness
(0.15%)
interaction
(1.00%)
interesting subject
(8.26%)
intrusive object presence
(-11.57%)
lively color
(10.64%)
low light
(4.15%)
noise
(-4.13%)
pleasant camera tilt
(-6.41%)
pleasant composition
(-48.93%)
pleasant lighting
(-17.52%)
pleasant pattern
(5.27%)
pleasant perspective
(-16.47%)
pleasant post processing
(-0.07%)
pleasant reflection
(-5.11%)
pleasant symmetry
(0.12%)
sharply focused subject
(1.03%)
tastefully blurred
(-22.22%)
well chosen subject
(-71.58%)
well framed subject
(-5.62%)
well timed shot
(-9.50%)
all
(-6.36%)
* 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.