Learn how to build your own personalized Deep Learning model for Intelligent door in less than a week. Lighting talk, CSPA AI Unconference @ Intel

1. Transfer Learning
for IoT
GEETA CHAUHAN JUNE, 2017

2. Intelligent Door
Welcome Sara
Good Kitty

3. What is
Deep
Learning?
 AI Neural Networks
composed of many
layers
 Learn like humans
 Automated Feature
Learning
 Layers are like Image
Filters

4. Tensorflow
Android Things
 Add Tensorflow Inferencing
library
 Create Inference object,
load model
 feed() image, run() to predict
 fetch() to get classification
result

5. Transfer
Learning
Transfer Learning – New Classifier
Inception V3

6. Optimizations
 Graph Transform Tool
 Freeze graph (variables to constants)
 Quantize weights (20 M weights for IV3)
 Quantization (32 bit float → 8 bit float)
 Memory Mapping
 Inception v3 93 MB → 1.5 MB

7. Intelligent Door Bell
Intel Edison + Seed Studio Kit Raspberry Pi + Pi Camera

8. Tensorflow IoT Pipeline
Inference on DeviceTrain Model in Cloud

9. Cucumber
Sorter

10. Caltrain Rider
 Realtime Caltrain arrival
prediction
 Audio Visual pipeline on
Raspberry pi
 Image classification for
Caltrains

11. And Some More …
Traffic sign
detector for
Self Driving Car
Wearable
assistant for
Blind (Horus)
Smart cameras,
smart door lock
Human line
counter
Real time
exercise score
on smart watch
Creative Arts,
Music
Generators
Intelligent
Robots

12. Look into the Future
 Hardware: Neural Network Chips
Intel Fathom Neural Stick Nvidia Jetson

13. References
 Tensorflow for Android Things Sample
 Tensorboard hands-on
 Graph Transform Tool
 Tensorflow for Poets (Transfer learning)
 Tensorflow for Poets2 (Optimizations for Mobile/IoT)
 Cucumber Farmer Deep Learning Story
 Caltrain Rider story
 Intel Fathom Neural Stick
 Nvidia Jetson

14. Questions?
Contact
https://www.linkedin.com/
in/geetachauhan/
geeta@svsg.co

15. Common Problem Solutions
 Tensorboard is your friend – X-Ray vision
 Image size mismatch for the input tensor
 Output classifier specify correct number of classes from your model
 Model too large – Load in memory
 Missing ops -- Graph transform tool
 Device heating up under heavy processing load
 Split the model, do part detection on device, rest in cloud
 Reduce the frequency eg only do on movement detection

16. Era of AI First
 Billions of connected devices
 Intelligence at the Edge
 Increasing Computation power
 Edison: 500 MHz, 1 GB RAM
 RPi3: 1.2 GHz Quad-core
 Deep neural networks running
on the IoT device
 Local inferencing →
compressed insights to cloud