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2019 Intelligent Cloud Conf - How Deep Learning is Saving the Planet

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Presented by Ganes Kesari at the Intelligent Cloud Conference, Copenhagen on 9-Apr-2019 (http://intelligentcloud.dk)

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2019 Intelligent Cloud Conf - How Deep Learning is Saving the Planet

  1. 1. Session Ganes Kesari Gramener How Deep Learning is Saving the Planet Session
  2. 2. 2 INTRODUCTION Co-founder & Head of Analytics “Simplify Data Science for all” 100+ Clients Insights as Stories Help apply & adopt Analytics Our data science platform, Gramex is now open- sourced!
  3. 3. 3 OUR STORY BEGINS 50,000 YEARS AGO… What happened to them? https://wattsupwiththat.com/2017/01/20/humans-not-climate-change-wiped-out-australian-megafauna/
  4. 4. 4 Humans are ecological serial killers…“ http://www.wrbh.org/wp-content/uploads/2018/05/Spaiens-book-cover.jpg …even with stone-age tools, our ancestors wiped out half the planet’s large terrestrial mammals. - Yuval Noah Harari
  5. 5. 5 CAN WE SAVE OUR BIODIVERSITY?
  6. 6. 6 SPOTTING, IDENTIFYING AND COUNTING ANIMALS TO SAVE THEM Gramener has partnered with Microsoft AI for Earth https://www.microsoft.com/en-us/ai/ai-for-earth
  7. 7. 7 MACHINE LEARNING 101 New Input Desired Outcome Machine learning how to do the job Known Input Known Outcome “Programs that solve the problem” “Programs that learn to solve the problem” vs
  8. 8. 8 WHY DEEP LEARNING? 8 Input Output Identify features to teach model Traditional Machine Learning Deep Learning Person Name Input Output Model automatically identifies features to learn Person Name https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
  9. 9. 9 NOT VERY DIFFERENT FROM HOW WE LEARN Training ..Versatile Detection!
  10. 10. 10 WHERE HAVE THE SALMON GONE? Source: Giphy (https://media.giphy.com/media/QM5GJO6J8lDfa/giphy.mp4)
  11. 11. 11 CAN YOU IDENTIFY THESE SALMON SPECIES? Sockeye Steelhead
  12. 12. 12 MONITORING SALMON MOVEMENT
  13. 13. 13 BUILDING THE MODEL – FASTER RCNN https://tryolabs.com/blog/2018/01/18/faster-r-cnn-down-the-rabbit-hole-of-modern-object-detection/ Region Proposal Network Region of Interest Pooling Regional CNN
  14. 14. 14 CLASSIFYING THE 12 SALMON SPECIES
  15. 15. 15 THE MODEL IN ACTION Video
  16. 16. 16 A VISUAL NARRATIVE OF THE ENGAGEMENT Microsoft published case study on this project at: https://partner.microsoft.com/en-us/case-studies/gramener/
  17. 17. 17 SPOTTING ELEPHANTS IN THE WILD Source: Giphy (https://media.giphy.com/media/1AHZzdXVTYDtJVTO5a/giphy.mp4)
  18. 18. 18 CAN YOU SPOT THE ELEPHANTS? https://www.savetheelephants.org/project/tsavo-aerial-defence/
  19. 19. 19 THE DATA & CHALLENGES Annotate
  20. 20. 20 MODEL BUILDING Region Proposal Network Region of Interest Pooling Regional CNN FASTER RCNN SSD MULTIBOX
  21. 21. 21 MODEL RESULTS
  22. 22. 22 CAN WE CLASSIFY ALL SPECIES ON EARTH? Source: Giphy (https://media.giphy.com/media/CFk1wEH7Cke0E/giphy.mp4)
  23. 23. 23 INATURALIST: FOSTERING CITIZEN SCIENCE https://play.google.com/store/apps/details?id=org.inaturalist.android&hl=en
  24. 24. 24 BUILDING THE MODEL Inception v4
  25. 25. 25 LIVE API DEMO
  26. 26. 26 LET’S SAVE THE PENGUINS Penguin populations are at risk in Antarctica and researchers need help to detect how it’s reducing over the years Source: Giphy (https://giphy.com/gifs/push-bwLowbhUWm2lO)
  27. 27. 27 TRACKING THE PENGUINS https://www.zooniverse.org/projects/penguintom79/penguin-watch/about/research ~100 cameras in 16 locations, and hourly images over years Crowd-sourced annotations to identify the penguins
  28. 28. 28 CROWD-SOURCED ANNOTATIONS https://www.zooniverse.org/projects/penguintom79/penguin-watch/classify
  29. 29. 29 APPROACHES TO COUNTING CROWDS Occlusion Density Difference Perspective Distortion Camera angle
  30. 30. 30 COUNTING USING DENSITY-BASED ESTIMATIONS Preserve spatial information Localize count Handle scale variations No longer looking for a head!
  31. 31. 31 APPROACH IN BRIEF https://arxiv.org/pdf/1707.09605.pdf Input an image Density Map Estimate the countSplit into 9 patches
  32. 32. 32 CHALLENGES WITH THE DATA Cleaned dataset: • Training: 18k • Validation: 3k • Test: 9k Hurdles • Camera angles, • Occlusion, • Perspective distortion, • Density difference, • Weather conditions Work done by Gramener in partnership with Microsoft AI for Earth
  33. 33. 33 MODEL ARCHITECTURE • High-level prior to classify image into buckets • Density estimation to create the density map • NC6 v3 virtual machine with V100 GPU card • Trained for 200 epochs, MAE for the model : ~10.5 https://arxiv.org/pdf/1707.09605.pdf Work done by Gramener in partnership with Microsoft AI for Earth
  34. 34. 34 WALKTHROUGH
  35. 35. 35 DEEP LEARNING TAKEAWAYS: WHEN THE RUBBER HITS THE ROAD • Acquire & clean data • Label your own data • Look out for practical data challenges • Don’t stop at counts - go for actionability • Build it into the user’s natural workflow • Sensitize users on low accuracy • Plan for model refresh
  36. 36. 36 The first step towards change is awareness. The second is acceptance. – Nathaniel Branden “ WildMe projects - https://www.wildbook.org/ https://www.flukebook.org/ THE WAY FORWARD..
  37. 37. 37 Session @kesaritweetsgramener.com @kesari Presentation deck with references at gkesari.com/intelligentcloud
  38. 38. Event partners Expo partners Expo light partners

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