AutoML Vision Object Detection enables developers to train custom machine learning models that are capable of detecting individual objects in a given image along with its bounding box and label.
The AutoML Vision Object Detection release includes the following features:
Object localization - Detects multiple objects in an image and provides information about the object and where the object was found in the image.
API/UI - Provides an API and custom user interface for importing your dataset from a Google Cloud Storage hosted CSV file and training images, for adding and removing annotations from imported images, for training and reviewing model evaluation metrics, and for using your model with online prediction.
AutoML Vision Edge now allows you to export your custom AutoML Vision Object Detection trained models.
- AutoML Vision Edge allows you to train and deploy low-latency, high accuracy models optimized for edge devices.
- With TensorFlow Lite, Core ML, and container export formats, AutoML Vision Edge supports a variety of devices.
- Hardware architectures supported: Edge TPUs, ARM and NVIDIA.
- To build an application on iOS or Android devices you can use AutoML Vision Edge in ML Kit. This solution is available via Firebase and offers an end-to-end development flow for creating and deploying custom models to mobile devices using ML Kit client libraries.
Quickstart: Label images by using the API in the Console
Quickstart: Label images by using the AutoML Vision Edge API
Quickstart: Label images by using the AutoML Vision API
Preparing your training data
Formatting a training data CSV
Annotating imported training images
Making individual predictions
Exporting Edge models