Lumos: A selfserve computer vision platform at AI NEXT conference
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Transcript of Lumos: A selfserve computer vision platform at AI NEXT conference
}Why Computer Vision?
• Enhanced photo / video search
Search photos posted by my friendscontaining a black bear
}Why Computer Vision?
• Enhanced photo / video search
• Detecting malicious content
• Helping visually impaired people
}Why Computer Vision?
• Enhanced photo / video search
• Detecting malicious content
• Helping visually impaired people
• Smart camera
Runs on Billions of images• Describes photos to the blind• Resurfaces notable memories • Provides better image and video search results• Protects people from objectionable content
More than 200 visual models• Currently trained and deployed• Dozens of teams across the company
self-serve build their own models
100+ Million examples in Lumosdatasets and growing fast
LumosLumos
DEEP RESIDUAL NETWORK
DEEP RESIDUAL NETWORK
TASK (T1)TASK (T1) TASK (T2)TASK (T2)
TRAINING:WEEKS
Lumos
DEEP RESIDUAL NETWORK
DEEP RESIDUAL NETWORK
DEEP RESIDUAL NETWORKDEEP RESIDUAL NETWORK
TASK (T1)TASK (T1)
nn
n-1n-1
22
11
TASK (T2)TASK (T2)
LESS COMPUTE/LESS ACCURACY
MORE COMPUTE/MORE ACCURACY
Lumos
DEEP RESIDUAL NETWORKDEEP RESIDUAL NETWORK
TASK (T1)TASK (T1)
nn
n-1n-1
22
11
TASK (T2)TASK (T2)
COMPUTE TIME:1-2 DAYS
ACCURACY:LESS
LumosLESS COMPUTE/LESS ACCURACY
MORE COMPUTE/MORE ACCURACY
DEEP RESIDUAL NETWORKDEEP RESIDUAL NETWORK
TASK (T1)TASK (T1)
nn
n-1n-1
22
11
TASK (T2)TASK (T2)
COMPUTE TIME:1 MONTH
ACCURACY:MORE
LumosLESS COMPUTE/LESS ACCURACY
MORE COMPUTE/MORE ACCURACY
DEEP RESIDUAL NETWORKDEEP RESIDUAL NETWORK
TASK (T1)TASK (T1)
nn
n-1n-1
22
11
TASK (T2)TASK (T2)
LESS COMPUTE/LESS ACCURACY
MORE COMPUTE/MORE ACCURACY
TASK (T3)TASK (T3) TASK (T4)TASK (T4) TASK (Tm)TASK (Tm)
Lumos
ACCU
RACY
COMPUTE
LumosTASK (T1)TASK (T1) TASK (T2)TASK (T2) TASK (T3)TASK (T3) TASK (T4)TASK (T4) TASK (Tm)TASK (Tm)
Lumos allows everyone at Facebook to build and deploy new computer vision models on the fly
• Collect training data for your new model• Train your new model at the right
accuracy/computational cost tradeoff• Refine your model based on live performance• Deploy your model to production
LumosLumos
On this DayOn this Day
AccessibilityAccessibility 360 Media Team360 Media TeamConnectivity LabConnectivity Lab
Protect and CareProtect and Care MomentsMomentsNews FeedNews Feed
Photo SearchPhoto Search
Lumos
• Indexing billions of photos• Finding similar photos in microseconds
Binary encoding
1011001011…01011110101101…00100001111000…10101111111001…00011010101010…10010001111110…10100101101001…11111001111000…10100001001001…0010
Compact representations
• Clusters hundreds of millions photos into millions of clusters• Approach: A fast binary k-means algorithm
– Works directly on similarity-preserving binary hashes of images. – Clusters image hashes into binary centers. – Builds hash indexes of binary centers to speedup computation.
Video Understanding
Objects: Dog, Cat..Shot boundary detection
Caption:Dog chasing cat in garden while people are laughing
Action: Chasing
Scene: Garden
Summarization
Saliency Detection
Dynamic Compression
FuturePrediction
Video Q&A