Drawing Classification and One-Shot Object Detection in Turi Create

Description: Apple’s open source toolset, Turi Create, recently added tasks for Core ML model creation including Drawing Classification and One-Shot Object Detection. Learn how to quickly use these capabilities in your apps as well as new techniques for visualizing and evaluating the performance of your custom models.

What’s Turi Create?

Turi Create is a Python library used for creating Core ML models. It has a simple, easy to use task-oriented API.

Tasks are our term for a collection of complex, machine learning algorithms abstracted away into a simple, easy to use solution for you to build your models.

Open source.

How to create a CoreML model

Five steps:

Identify the task, aka what problem we’re trying to solve
Collect data
Train a model
Test/evaluate the model
Deploy the model

Here’s the code (in python):

import turicreate 

// Load data 
data = turicreate.SFrame("ActivityData.sframe")
train, test = data.random_split (0.8) 

// Create a model 
model = turicreate.activity_classifier.create(train, 

// Evaluate the model
metrics = model.evaluate(test) 

// Export for deployment 
model.export_coreml("MyActivityClassifier.mlmodel")

Task types


Current	New

One-Shot Object Detection

The one-shot object detection is like object detection, but requires much less train data.

Let’s say that we want to detect objects like cards and logos with our camera. These features are 2D features:
One-Shot Object Detection is able to take advantage of these characteristics of regularity and consistency to dramatically reduce the amount of data needed to build an accurate model.

Object Detection normal approach:
Take plenty of pictures, annotate all the pics with the object location and tag said object:

One-shot approach: One clear picture per class

This works with a process called Synthetic Data Augmentation. Turi Create ships with a collection of images of the real world. The script takes the provided image and overlay onto these real-world images along with various color perturbations and distortions to simulate real world effects. The script also takes care of annotating these images.

On the session demo the Turi script has created 2000 images per class (and annotate them etc).

One-shot vs traditional approach: