WannaEat: A computer vision-based, multi-platform restaurant lookup app

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"Developing a new product nowadays can be a challenge. A service cannot survive without supporting multiple platforms, having a smooth user experience, and providing features that just work. During this presentation, you can see how such a prototype was built in 10 days, rethinking best practices and introducing a novel approach for the restaurant suggestion. A number of technologies and platforms are covered, including service-side development with NodeJS, iOS and Android development with React-Native, and image processing with TensorFlow."

Technology

Oct.28.2017
Steve, Cedric, Liboon
Rakuten, Inc.

3
Stevche Radevski
Msc Software Engineering
Working on internal business
support/analytics tools.
Mainly working with JavaScript
(React, React Native, NodeJS)
Cedric Konan
Msc Ubiquitous Computing
Working on Dining services
tools.
Mainly working with Java but
a php lover
Tan Li Boon
Bsc Computer Science
Working on big data with
dev-ops.
Mainly working with Java
(Spark, Couchbase, Hadoop)

5
Given 3 months to do anything we want!
Every Friday.
Any topic.
Work however we want.
FREEDOM!

6
Play around with Machine Learning
Do something differently
Utilize everyone’s specialized skills and learn from each other
Multiplatform mobile app development

14
Fast Development
Multi-platform
Easily-interfaceable
Plug and play

15
A JavaScript based framework to build native, multi-platform mobile applications
1487 official contributors on GitHub
1000+ mobile apps created with it

16
Easy to use (especially if you know React)
Based on JavaScript
Uses markup language syntax
Reusable Components
Very well supported (documentation, active community)

17
App Result list
Star Rating Component
Row component
List View

20
Recognize what a restaurant sells based on their
food pictures!

23
No billing surprises
Demonstration of expertise
Specialized Models
A technology company should maintain in-house infrastructure

24
References:
Yoshiyuki Kawano and Keiji Yanai,
The University of Electro-
Communications, Tokyo, Japan
256 food categories, 100
images per category
UEC FOOD-256

25
Inception-v3 is a convolution-based neural network (ConvNet).
It takes 2 to 3 weeks on multiple GPUs to train a ConvNet from zero!
If you need to tweak the network parameters, you have to re-train the whole thing.
Clearly this is not reasonable.
Enter Transfer Learning…

27
Use the outputs of another trained network as generic image feature detectors,
and train a new shallow model using these outputs.
softmax
conv2
conv1
Images and tags
loss
softmax
conv2
conv1
Images and tags
loss
Original Target
Pre-trained

32
Google Vision API
Mine Restaurants Data
Backend API
Vision API
WannaEat Vision
Restaurants.json
Get Tags Per Image
Per Restaurant
Image – Tag
Dictionary
1
2
3
4
5
7
8
6
Store reduced
tags per restaurant
Get 1000 restaurants from Google Maps
Generate tags list
per image

33
Mobile
Google Vision API
Backend API
Vision API
WannaEat Vision
Restaurants.json
User uploaded Image
Image or Tag
Top tag
for image
Matching
Restaurants

36
Collocation is great (no communication friction)
Freedom: Possibility to choose topic and tech stack.
Small team, so no need for long meetings, tickets, wikis (Trello and p2p talk).
Clear system boundaries with clear interfaces between each boundary.
Well-defined responsibilities (but still helping each other)

39
Took some time to remember what we did the last week
Collecting data for restaurants was time-consuming
Vision processing took equally long.
Spent half the time to figure out what problem to tackle

41
Of course we did!
Work in small teams!
Have clearly defined responsibilities
Do one thing at a time (switching between projects takes mental effort)
Machine Learning is not difficult anymore (many API as a service providers)
Rethink best practices (both development and UI/UX)

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1. Oct.28.2017 Steve, Cedric, Liboon Rakuten, Inc.

2. 2

3. 3 Stevche Radevski Msc Software Engineering Working on internal business support/analytics tools. Mainly working with JavaScript (React, React Native, NodeJS) Cedric Konan Msc Ubiquitous Computing Working on Dining services tools. Mainly working with Java but a php lover Tan Li Boon Bsc Computer Science Working on big data with dev-ops. Mainly working with Java (Spark, Couchbase, Hadoop)

4. 4

5. 5 Given 3 months to do anything we want! Every Friday. Any topic. Work however we want. FREEDOM!

6. 6 Play around with Machine Learning Do something differently Utilize everyone’s specialized skills and learn from each other Multiplatform mobile app development

7. 7

8. 8 • • • • • • • • ’

9. 9

10.

11. 11

12. 12

13. 13 …

14. 14 Fast Development Multi-platform Easily-interfaceable Plug and play

15. 15 A JavaScript based framework to build native, multi-platform mobile applications 1487 official contributors on GitHub 1000+ mobile apps created with it

16. 16 Easy to use (especially if you know React) Based on JavaScript Uses markup language syntax Reusable Components Very well supported (documentation, active community)

17. 17 App Result list Star Rating Component Row component List View

18. 18

19. 19

20. 20 Recognize what a restaurant sells based on their food pictures!

21. 21

22. 22

23. 23 No billing surprises Demonstration of expertise Specialized Models A technology company should maintain in-house infrastructure

24. 24 References: Yoshiyuki Kawano and Keiji Yanai, The University of Electro- Communications, Tokyo, Japan 256 food categories, 100 images per category UEC FOOD-256

25. 25 Inception-v3 is a convolution-based neural network (ConvNet). It takes 2 to 3 weeks on multiple GPUs to train a ConvNet from zero! If you need to tweak the network parameters, you have to re-train the whole thing. Clearly this is not reasonable. Enter Transfer Learning…

26. 26

27. 27 Use the outputs of another trained network as generic image feature detectors, and train a new shallow model using these outputs. softmax conv2 conv1 Images and tags loss softmax conv2 conv1 Images and tags loss Original Target Pre-trained

28.

29.

30. 30

31. 31

32. 32 Google Vision API Mine Restaurants Data Backend API Vision API WannaEat Vision Restaurants.json Get Tags Per Image Per Restaurant Image – Tag Dictionary 1 2 3 4 5 7 8 6 Store reduced tags per restaurant Get 1000 restaurants from Google Maps Generate tags list per image

33. 33 Mobile Google Vision API Backend API Vision API WannaEat Vision Restaurants.json User uploaded Image Image or Tag Top tag for image Matching Restaurants

34. 34

35. 35

36. 36 Collocation is great (no communication friction) Freedom: Possibility to choose topic and tech stack. Small team, so no need for long meetings, tickets, wikis (Trello and p2p talk). Clear system boundaries with clear interfaces between each boundary. Well-defined responsibilities (but still helping each other)

37. 37

38. 38 Nothing, we are that good!

39. 39 Took some time to remember what we did the last week Collecting data for restaurants was time-consuming Vision processing took equally long. Spent half the time to figure out what problem to tackle

40. 40

41. 41 Of course we did! Work in small teams! Have clearly defined responsibilities Do one thing at a time (switching between projects takes mental effort) Machine Learning is not difficult anymore (many API as a service providers) Rethink best practices (both development and UI/UX)

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