Slides describing WiDS (Women in Data Science) 2019 datathon hosted on Kaggle, and one approach to solving the image classification problem

1. WiDS Datathon 2019
Vani Mandava
Director, Data Science,
Microsoft Research
@vanimt

2. • Oil Palm is the tree that produces palm oil
• Mostly grown in Africa and South and Central
America
• Deforested land to grow by 53 million hectares
by 2050
• Important to understand where these oil palm
plantations are
• High resolution satellite images combined with
computer vision algorithms can help

3. 80% decline in orangutan population in Borneo

4. How to map these
plantations?

5. Datathon Challenge
Planet and Figure Eight in collaboration with
West Big Data hub and WiDS Datathon committee
created a dataset of hi-res satellite imagery
Problem:
Develop a model to detect whether oil palm
plantations are present or not

6. Dataset on Kaggle
• Supervised Learning
problem
• 15K images in training
set
• 4K images in test set

7. One approach

8. Microsoft Custom Vision
• Azure hosted free platform service to build computer vision
model
• Great learning tool for beginners
• Uses AlexNet based Convolutional Neural Networks

10. Custom vision
• Custom Vision Service (UI or API)
1. UI – upload, train and test your model using the UI
2. API – use the Azure Python SDK to train and test the model in Python
3. Hybrid – Train the model using Custom vision UI and export to your
favorite model such as Tensorflow to run Android apps with the
model
Use any of the above options to use the Custom Vision service. I
found the API – option #2 to be most effective

11. Train and Test using custom
vision UI

12. Download
Kaggle
images locally

13. Manually uploaded images for training

15. Test

16. Python SDK

17. Steps
1. Install the Custom Vision SDK for Python
2. Get the training and prediction keys
3. Get the training images
4. Upload and tag images (hasoilpalm, nooilpalm)
5. Train the classifier
6. Get and use the default prediction endpoint

18. Upload and tag all images

19. Verify training with subset of images

20. Sample of the Prediction output
Prediction
output

21. Hybrid approach
Train in Custom Vision UI and export the model to Tensorflow

22. creates .pb file
Export custom vision model to Tensorflow

24. Run Tensorflow model

25. Python sample code for Tensorflow

26. Good luck!
Vani Mandava
Microsoft Research
@vanimt
I’ll buy lunch for the first team who gets to .98+ AUC using Microsoft Custom
vision
(restaurant of your choice!)