![]() You'll need to organize your annotations in a JSON file. While collecting training images, it's also okay to include some images that only have a single label, like a photo of just aloe. In the next image, I have a person holding a cactus in a pot. For example, here I have an image of Haworthia, Jade, and Aloe in pots on a window sill. I decided to have a bit of fun and build a classifier that detects multiple succulent plants in different scenes. As usual, the first thing I'll need to do is collect some training data. For example this image contains a dog, toy, grass, and park. It allows you to predict a set of objects, attributes, or labels for your images. ![]() That's where the new multi-label image classifier comes in. I can't really draw a bounding box to represent that the dog is in a park or outdoors. Now, this is great, but I'm also interested in the scene that the objects are in. For example, I've drawn a bounding box around the dog and another one around the ball. If you're interested in objects, then you can use the object detector to locate objects within a scene. For example, you might describe this image as dog or maybe outdoors. Before I get there, recall that single-label image classification is designed to predict the best label describing the contents of an image. Now that I've covered improvements in Create ML, I want to talk about the new multi-label image classifier. On top of general improvements, this can boost the accuracy of your classifier, lead to faster training time, and reduce the memory needed to store the extracted features. The new feature extractor has a smaller output embedding size when compared to our previous version. In the Create ML app, you'll notice a new feature extractor option in the model parameters section of the Settings tab. You can find out more by checking out our article on the machine learning research website. Image understanding models in the OS continue to evolve to give you the best possible experience. The latest version of the Apple Neural Scene Analyzer is now available to you for building state-of-the-art models with very little training data. The image classifier in Create ML is designed to help you build models to answer the question, what is the best label to describe the contents of my image? Similar to the text classifier, the image classifier leverages a pre-trained model to extract relevant information from images. Next, I want to talk about how we use transfer-learning in the image classification task. Make sure to watch "Explore Natural Language multilingual models" to find out more. We have a whole video that covers all of the details. You can leverage BERT on iOS 17, iPadOS 17, and macOS Sonoma. On top of supporting multilingual text classifiers, BERT can also boost the accuracy of your monolingual text classifiers. The BERT embedding model is multilingual, which means your training data can now contain more than just one language. You can find the new option in the Create ML app in the model parameters section of the Settings tab. It's a bidirectional encoder representations from transformers model, or BERT for short. This year, we designed a new embedding model and trained it on billions of labeled text examples. You can choose the transfer-learning algorithm that uses a pre-trained embedding model as a feature extractor. In this example, I have sports, entertainment, and nature labels. To train such a model, all you need to do is provide it with a table of text and label pairs. A text classifier is a machine learning task designed to recognize patterns in natural language text. Let's get started with Text Classification improvements. And lastly, I'll talk about new Augmentation APIs that we designed to help you improve the quality of your model when you're limited on training data. Then I'll introduce a brand-new way of building machine learning models to understand scenes with multiple labels. I'll start by going through improvements that we've made to Create ML. Create ML gives you access to our latest technology so you can build your own custom machine learning experiences, without the hassle. We've gone through the process of creating state-of-the-art models that power many features, like the Search experience in the Photos app and Custom Sound Recognition in Accessibility. Our goal is to give you the tools to build great apps that use machine learning without all of the overhead. Training a large-scale model from scratch can take thousands of hours, millions of annotated files, and expert domain knowledge. I'm excited to take you on a tour of what's new. We've been working on some great improvements to the Create ML app and frameworks. I'm a machine learning engineer on the Create ML team.
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