Models

Built for the Edge TPU

In the lists below, each "Edge TPU model" link provides a .tflite file that is pre-compiled to run on the Edge TPU. You can run these models on your Coral device using the scripts shown in API demos. (Remember to also download the model's corresponding labels file.)

For many of the models, we've also provided a link for "All model files," which is an archive file that includes the following:

  • Trained model checkpoints
  • Frozen graph for the trained model
  • Eval graph text protos (to be easily viewed)
  • Info file containing input and output information
  • Quantized TensorFlow Lite model that runs on CPU (included with classification models only)

Download this "All model files" archive to get the checkpoint file you'll need if you want to use the model as your basis for transfer-learning, as shown in the tutorials to retrain a classification model and retrain an object detection model.

Notice: These are not production-quality models; they are for demonstration purposes only.

To build your own model for the Edge TPU, you must use the Edge TPU Compiler.

All models trained on ImageNet used the ILSVRC2012 dataset.

Image classification


Object detection


MobileNet SSD v2 (Faces)

Detects the location of human faces
Dataset: Open Images v4
Input size: 320x320
(Does not require a labels file)

Embedding extractor (classification)


All models


TensorFlow Lite models

You can download all the above pre-compiled Edge TPU models and corresponding labels files with the following link.

This also includes each corresponding quantized TensorFlow Lite model that runs on the CPU so you can compare performances.