Easier object detection on mobile with TensorFlow Lite

At Google I/O this year, we are excited to : a step-by-step tutorial on how to train and deploy a custom object detection model on mobile devices with no machine learning expertise required.

Despite being a very common ML use case, object detection can be one of the most difficult to do. We've worked hard to make it easier for you, and in this blog post we'll show you how to leverage the latest offerings from TensorFlow Lite to build a state-of-the-art mobile object detector using your own domain data.

On-device ML learning pathway: learn how to train and deploy custom TensorFlow Lite object detection model in 12 minutes.

Training a custom object detection model and deploying it to an Android app has become super easy with TensorFlow Lite. We released a learning pathway that teaches you step-by-step how to do it.

In the video, you can learn the steps to build a custom object detector:

1. Prepare the training data.
2. Train a custom object detection model using TensorFlow Lite Model Maker.
3. Deploy the model on your mobile app using TensorFlow Lite Task Library.

There’s also a your feedback!

EfficientDet-Lite: the state-of-the-art model architecture for object detection on mobile devices

Running machine learning models on mobile devices means we always need to consider the trade-off between model accuracy vs. inference speed and model size. The state-of-the-art mobile-optimized model doesn’t only need to be more accurate, but it also needs to run faster and be smaller. We adapted the neural architecture search technique published in the . You also can train EfficientDet-Lite custom models using your own training data with TensorFlow Lite Model Maker.

TensorFlow Lite Model Maker: train a custom object detection using transfer learning in a few lines of code

format and the Cloud AutoML’s or to learn more.

TensorFlow Lite Task Library: deploying object detection models on mobile in a few lines of code

to learn more about the customization options in Task Library, including how to configure the minimum detection threshold or the maximum number of detected objects.

TensorFlow Lite Metadata Writer API: simplify deployment of custom models trained with TensorFlow Object Detection API

Task Library relies on the .

For example, if you train a model using into the [0..1] range. You need to specify normalization parameters to be the same as in the preprocessing logic used during the model training.

See this team to bring more object detection model architectures to Model Maker. We will also continue to work with researchers in Google to make future state-of-the-art object detection models available via Model Maker, shortening the path from cutting-edge research to production for everyone. Stay tuned for more updates!