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
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
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
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
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
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
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
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
SPOTTING ELEPHANTS IN THE WILD
Source: Giphy (https://media.giphy.com/media/1AHZzdXVTYDtJVTO5a/giphy.mp4)
18. 18
CAN YOU SPOT THE ELEPHANTS?
https://www.savetheelephants.org/project/tsavo-aerial-defence/
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)
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
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
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
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..