Utilities to help process a dataset.


Reads labels from a text file and returns it as a dictionary.

This function supports label files with the following formats:

  • Each line contains id and description separated by colon or space. Example: 0:cat or 0 cat.

  • Each line contains a description only. The returned label id’s are based on the row number.


file_path (str) – path to the label file.


Dict of (int, string) which maps label id to description.


Utilities for using the TensorFlow Lite Interpreter with Edge TPU.


Loads the Edge TPU delegate with the given options.

pycoral.utils.edgetpu.make_interpreter(model_path_or_content, device=None)

Returns a new interpreter instance.

Interpreter is created from either model path or model content and attached to an Edge TPU device.

  • model_path_or_content (str or bytes) – str object is interpreted as model path, bytes object is interpreted as model content.

  • device (str) –

    The type of Edge TPU device you want:

    • None – use any Edge TPU

    • ”:<N>” – use N-th Edge TPU

    • ”usb” – use any USB Edge TPU

    • ”usb:<N>” – use N-th USB Edge TPU

    • ”pci” – use any PCIe Edge TPU

    • ”pci:<N>” – use N-th PCIe Edge TPU


New tf.lite.Interpreter instance.

pycoral.utils.edgetpu.run_inference(interpreter, input_data)

Performs interpreter invoke() with a raw input tensor.

  • interpreter – The tf.lite.Interpreter to invoke.

  • input_data – A 1-D array as the input tensor. Input data must be uint8 format. Data may be Gst.Buffer or numpy.ndarray.