"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

December 31, 2022

Building Vision Models - Myth vs Reality

Challenge / Perception - Customer feels we need millions of images 

Reality - We do not need to wait for perfect data, Data collection, or Synthetic data creation everything is a going process

Challenge / Perception  - Data collection is effortless, It can be done by google search / kaggle

Reality - The real world and the kaggle dataset are miles apart. Real-world challenges are dependent on light/angle/hardware used. Buying data is even more expensive :). Data cost is more costly than model training time

Challenge / Perception  - I need the start of the art model with 99% accuracy / Can we get a performance like the state of the art? 

Reality - We need to be realistic with the data we have, and an incremental model that we can develop. 

Challenge / Perception - Model development is a one-time effort. Collect / Build / Deploy / Move on 

Reality - Base model / retrain / field test and next version is incremental effort. ML is an iterative incremental effort. It has a set of parallel ongoing efforts like below


When the customer wants state of art but has no strategy on how they need to incrementally build upon becomes an effective challenge to provide the vision/clarity.

The building is easy sometimes vs Selling is hard many times. 

Keep Thinking!!!


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