Teaching a Fridge to See
http://slides.com/dpeskov/deck/live
About Me
- Work for PwC in D.C.
( Analytics Innovation Accelerator)
- Computer Scientist
- Have used refrigerator
The Project
Build a fridge system that is:
-
Real-time
-
Global
-
"Smart"
Agenda
- Machine Vision Overview
- Implementation
- Leveraging the Cloud
Why Should You Care?
- “Computers now better than humans at recognising and sorting images” -The Guardian. May 2015
- [Artificial Intelligence is our] “greatest existential threat” - Elon Musk
Deep Learning for Machine Vision
How to classify this:
Failure:
Images are UNSTRUCTURED DATA
- Computationally expensive
- Relevant and irrelevant data combined
What is Deep Learning?
The Stack
The Tools
Implementation
IoT, Data Generation, Model Building
Output
Set-Up
Data Generation
Factors to consider:
- Sample size
- Duplication issues
- Backgrounds
- Number of classes
- Lighting
- Angles
Model Building
Two Tasks:
Model Approach:
vs
1)
2)
vs
Region Identification
Selective Search
rCNN
Remove
Overlap
Classify Object
One Step!
Leveraging the Cloud
Building an App
- Flask
- Creating a python environment
- Version control with Git
- Documentation
Cloud GPU
- 6x faster
- Cheap
- Reliable
The Interface
Good
Fast
Cheap
Easy - (er)
Painful
Back up your data!
Work together
Overfitting
Lessons Learned
Thank you!
denis.peskov@pwc.com
SmartFridge_Nimbix_3.15.16
By dpeskov
