"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" ;

August 30, 2021

Vision - Webinar Notes - Life of Vision Project - OpenCV - Labelmg - Object Detection - Tensorflow Lite - KfServing - MySQL

Some talks summarize all of your work, plus talk on those aspects which you did implicitly part of your work. Nice Talk to connect with Vision Work :)

Key Notes
  • Workflow for Image Solutions
  • Dataset preparation
  • Data Annotation
  • Training / Benchmark
  • Pre-trained + Transfer Vs Custom Model
  • Metrics for benchmarking

Data Collection
  • Data set type
  • Streaming or Image
  • Data formats
  • Single image / frames
  • Video - Frame - Feed model
  • Image Resolution / Frame rates sampling
  • Reduce frame rate to support more streams
  • Preprocessing work
  • Crop noisy areas
  • Select areas of interest
  • Data Generation
  • Data Augmentation
  • Simple techniques including vision tricks - rotation, transformation, different angles
  • GAN / Synthetic data generation techniques

Data Annotate 
  • Annotate / Review
  • Validate with SME
  • Bounding boxes / Segmentation / Labels
  • Single / Multiple objects / Classes
  • Occlusion, Light, settings
  • Partially available surfaces
  • Fine-grained annotation or not
  • Data set representation against bias
  • Coverage of possible classes
  • Models for Day time vs Night time

Model Training
  • Segmentation / Custom Detection
  • Post-processing
  • Transfer Learning
Model Optimization
  • Prune / Quantize
  • Inference Engines
  • CPU / GPU / FPGA
Benchmark
  • Testing on Deployable hardware
  • Number of endpoints
  • Load vs Response
  • Re-annotate / Re-train
  • Ensemble or Single Model
Deploy
  • Edge vs Cloud 
  • Edge Server - Lite weight models
  • Address based on the constraint, workloads for edge devices
  • Hybrid approach both edge + cloud
  • Model interface with application
  • Storing Results in DB
  • Real-time notification or just store
Model Monitoring
  • Monitoring for data/accuracy of detections
  • Pick low accuracy results / retrain them
  • Capture when confidence is less than 50%
  • Continuous re-learning 

End to End platform for this

Keep Connecting the Dots!!!

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