AutoML Vision Edge
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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