AutoML Vision Edge

Create custom image classification models from your own training data with AutoML Vision Edge.

If you want to recognize contents of an image, one option is to use ML Kit's on-device image labeling API or on-device object detection API. The models used by these APIs are built for general-purpose use, and are trained to recognize the most commonly-found concepts in photos.

If you need a more specialized image labeling or object detection model, covering a narrower domain of concepts in more detail—for example, a model to distinguish between species of flowers or types of food—you can use Firebase ML and AutoML Vision Edge to train a model with your own images and categories. The custom model is trained in Google Cloud, and once the model is ready, it's used fully on the device.

Get started with image labeling Get started with object detection

Key capabilities

Train models based on your data

Automatically train custom image labeling and object detection models to recognize the labels you care about, using your training data.
Built-in model hosting

Host your models with Firebase, and load them at run time. By hosting the model on Firebase, you can make sure users have the latest model without releasing a new app version.

And, of course, you can also bundle the model with your app, so it's immediately available on install.

Implementation path

Assemble training data Put together a dataset of examples of each label you want your model to recognize.
Train a new model In the Google Cloud console, import your training data and use it to train a new model.
Use the model in your app Bundle the model with your app or download it from Firebase when it's needed. Then, use the model to label images on the device.

Pricing & Limits

To train custom models with AutoML Vision Edge, you must be on the pay-as-you-go (Blaze) plan.

Datasets Billed according to Cloud Storage rates
Images per dataset 1,000,000
Training hours No per-model limit

Next steps