1. The document discusses how to create a machine learning model for image classification using Core ML on a Mac. 2. It provides steps for preparing a dataset with labeled images, training a model, exporting the model, and demoing the model. 3. The document recommends using at least 10 images per label for the training set and images that are at least 299x299 pixels.

1. “Any kid can do it. “ 👧👦
Create ML 🤖
Hanawa Takuro LIFULL Co., Ltd.

2. $Hanawa Takuro
facebook.com/takuro.hanawa
🏢LIFULL Co.,Ltd.
iOS Engineer
/ WWDC 🤵🌹Bachelor Japan

4. Image Classification 🌉
“ ”

5. Agenda
• Preparing the data set 🌉
• Start training 🤖
• Export model 🖨
• Demo 💪

6. Preparing the data set 🌉
•
• 10
( 😇)
•
Use at least 10 images per label for the
training set, but more is always better. Also,
balance the number of images for each label.

7. Preparing the data set 🌉
• 299x299px
•
•
The images don’t have to be the same size as
each other, nor do they have to be any
particular size, although it’s best to use
images that are at least 299x299 pixels.

9. 😪
/

10. Preparing the data set 🌉
• ⭐ ImageNet: http://www.image-net.org/
• Open Images Dataset: https://
storage.googleapis.com/openimages/
web/index.html
• Kaggle Datasets: https://
www.kaggle.com/datasets

11. Preparing the data set 🌉
•
•
• 1:9

12. Start training 🤖
• macOS 10.14+
• Xcode 10.0+
• macOS Target playground

13. Start training 🤖

14. 🙇

15. Export model 🖨

16. 🙇

17. /
❌ ❌❌
😂

18. Demo 💪
Qiita: Create ML
https://qiita.com/hanawat/items/63176648a52b5e0b985b
Qiita: What’s New in Swift4.2
https://qiita.com/hanawat/items/4605f9e357c8794b58d7