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Deep Learning in the Connected Kitchen or Launching a Computer Vision program in a new vertical Hristo Bojinov, CTO Company Vision The Problem Food People disconnect Not-so-smart smart kitchen Food info not available, not


  1. Deep Learning in the Connected Kitchen or “Launching a Computer Vision program in a new vertical” Hristo Bojinov, CTO

  2. Company Vision

  3. The Problem Food ↔ People disconnect Not-so-smart “smart kitchen” Food info not available, not actionable

  4. What We Do Food, personalization, technology “Give food a voice” ( ⇒ Computer Vision is essential) Icons made by Madebyoliver, Popcorn Arts, Freepik from www.flaticon.com are licensed by CC 3.0 BY

  5. Computer Vision at Innit Helps us understand users ❖ Inventory, behaviors, multi-sensor fusion, market analytics ❖ And, build a delightful user experience Applications in storage and processing ❖ Recognize and act on food state ❖ Visible light, depth, IR

  6. Program Logistics Multi-site program (HQ, academia) Food Recognition service (AWS) ❖ G2 instance backend (blend of CPU and GPU workload) ❖ Frontend orchestrates auto and manual processing ❖ Service API for 3rd party use

  7. CV Tech: Food Recognition System

  8. CV Tech: Food Recognition System

  9. CV Tech: Food Recognition System Data is King!

  10. CV Tech: Object Detection Stage

  11. CV Tech: Object Detection Stage

  12. CV Tech: Object Detection Stage

  13. CV Tech: Object Detection Stage DetectNet ➔ Easy setup and initial training ➔ Python layers, “low resolution” Faster-RCNN ➔ Multi-phase training/tuning ➔ High resolution & recall 😁 DeepMask & SharpMask

  14. CV Tech: Object Detection Stage

  15. CV Tech: Classification Stage

  16. CV Tech: Classification Stage

  17. CV Tech: Classification Stage

  18. CV Tech: Classification Stage Controlled scene layout ⇒ precision In-house data collection and tools Command-line → DIGITS AlexNet → VGG

  19. CV Tech: Product DB Image Retrieval

  20. CV Tech: Product DB Image Retrieval ❖ Exact product (or attribute) matching ❖ KAZE descriptors (GPU acceleration WIP) ➢ Current need to balance CPU/GPU ➢ Order-of-magnitude acceleration ❖ Hierarchical analysis in the pipeline

  21. CV Research: Training on Synthetic Sets

  22. CV Research: Text Extraction

  23. In a nutshell... ❖ Focus on differentiated capabilities, in the food space ❖ Tie in with all stages of human ↔ food interaction ❖ Fusion of images & other “sensors” ❖ GPU tech a strong enabler

  24. Takeaways ❖ Objectives → domain constraints ( good! ) ❖ Sources of initial training+test data; build tools ❖ Hardware (local experiments OK, cloud for serving) ❖ Software (don’t get tied to a framework; abstract away)

  25. We are hiring! 🚁 hristo@innit.com

  26. About Innit ❖ Inform and elevate the interaction between people and food ❖ 4+ years in the making, substantial funding, IP & tech ❖ Pirch SOHO, ShopWell About the Speaker ❖ Embedded & Security ❖ Android, Computer Vision ❖ Computer technology at Innit

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