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@binhyencoem: 🌧️

𝘽𝙤𝙮👤
𝘽𝙤𝙮👤
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Region: VN
Thursday 24 September 2026 05:01:38 GMT
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Comments

khgcoten.18
18. :
suy ngang luon tus ơi
2026-09-24 06:12:54
3
m.lovetitok
m. :
Xhhhhh
2026-09-24 09:28:49
3
kn_200308
Nguyễn Xuân Kiên :
anh mong.... anh mong điều gì? mong chờ em luôn vui và cười như lúc này##
2026-09-24 11:25:14
1
lehaudh1401
Buồn 🍃 :
Xhh
2026-09-24 05:48:49
1
n.trc690
nờ trúc 🧚 :
2026-09-24 05:08:43
1
congminh..1
Minh tinh. :
tus ơi em nên tim kh😔
2026-09-24 18:21:01
0
quangtuan0906
Đồng Quang Tuấn :
Xhhh
2026-09-24 16:35:35
0
tehaiva66
chồng cũ :
toii thich ban ma bann ngo qua ban cha de y gi ca
2026-09-24 17:17:05
0
user2061280105226thuhuye
ThUu Huyền z😉 :
hộ em vs với ạ
2026-09-24 05:03:23
0
hanem766
🖤 :
hộ dlai vs
2026-09-24 09:37:13
0
buon.qua032
buon🖤 :
Chéo vd hong ạ
2026-09-24 05:17:46
0
am.m.m.nhc88
đam mê âm nhạc :
[Ấm lòng]
2026-09-24 05:18:27
0
phloc2120
Phú Lộc :
😌😌😌
2026-09-24 13:42:07
0
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Nếu bạn đang yêu thích một note hương nào đó, có thể video này sẽ giúp được bạn ☺️ #nuochoanam #nuochoa #apaniche
Nếu bạn đang yêu thích một note hương nào đó, có thể video này sẽ giúp được bạn ☺️ #nuochoanam #nuochoa #apaniche
🆔: CBR-XHE-CNQ Video autoral liberado para shopee video edite e poste. me siga para mais conteúdo. NÃO AUTORIZO PARA USAR EM PACK DE VIDEOS #afiliadoshopee #videoautoral #shopeevideo #afiliadosiniciantes #jogodepanelas
🆔: CBR-XHE-CNQ Video autoral liberado para shopee video edite e poste. me siga para mais conteúdo. NÃO AUTORIZO PARA USAR EM PACK DE VIDEOS #afiliadoshopee #videoautoral #shopeevideo #afiliadosiniciantes #jogodepanelas
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#audit #foryou #fyp #usa #cops
HAPPY HOUR selama 28 JAM🔥👀 Jangan lepaskan peluang keemasan ni! #ulikehairremoval #buangbulu #hairremoval #ulike #fnalainams #fypviralシ #racuntiktok #promosi #paydaysale
HAPPY HOUR selama 28 JAM🔥👀 Jangan lepaskan peluang keemasan ni! #ulikehairremoval #buangbulu #hairremoval #ulike #fnalainams #fypviralシ #racuntiktok #promosi #paydaysale
A road infrastructure client was bidding for a city project against vendors promising cameras and image recognition. They had sensors in the ground and no vision model at all. I got two sprints of two weeks, final presentation included. The first decision was to train on the categories the buyer already files. Longitudinal cracks, transverse cracks, alligator cracks, faded lane lines, faded crosswalks and manhole covers. ⠀ I did not invent a taxonomy, because matching the customer vocabulary is what makes the output usable on the day it lands. The second decision was that detection alone is not a deliverable. Every image carried GPS metadata, so I plotted each detection on a map, colour coded by damage type, with a checkbox filter per category and a timestamp on each point. The people in that room read a map and a filter. A confusion matrix would not have moved the conversation one inch. ⠀ The third decision was to keep the stack boring on purpose. A standard object detection model fine tuned on images the client already had, a common inference server pulling weights from object storage, and a lightweight web front end behind one API. Nothing on that list was novel, which is exactly why it was finished in time to present. I also handed over the retraining, so they could add a new damage category without calling me. What would you cut first if your demo was in four weeks? ⠀ Follow @gamechangerai for more. ⠀ #ai #machinelearning #computervision #objectdetection #smartcity
A road infrastructure client was bidding for a city project against vendors promising cameras and image recognition. They had sensors in the ground and no vision model at all. I got two sprints of two weeks, final presentation included. The first decision was to train on the categories the buyer already files. Longitudinal cracks, transverse cracks, alligator cracks, faded lane lines, faded crosswalks and manhole covers. ⠀ I did not invent a taxonomy, because matching the customer vocabulary is what makes the output usable on the day it lands. The second decision was that detection alone is not a deliverable. Every image carried GPS metadata, so I plotted each detection on a map, colour coded by damage type, with a checkbox filter per category and a timestamp on each point. The people in that room read a map and a filter. A confusion matrix would not have moved the conversation one inch. ⠀ The third decision was to keep the stack boring on purpose. A standard object detection model fine tuned on images the client already had, a common inference server pulling weights from object storage, and a lightweight web front end behind one API. Nothing on that list was novel, which is exactly why it was finished in time to present. I also handed over the retraining, so they could add a new damage category without calling me. What would you cut first if your demo was in four weeks? ⠀ Follow @gamechangerai for more. ⠀ #ai #machinelearning #computervision #objectdetection #smartcity
New Arrival modern coord set guys. #capcut #viral #coord #trending #fyp
New Arrival modern coord set guys. #capcut #viral #coord #trending #fyp

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