This page shows how you can start running TensorFlow Lite models with Python in just a few minutes. All you need is a TensorFlow model converted to TensorFlow Lite. (If you don't have a model converted yet, you can experiment using the model provided with the example linked below.)
Install just the TensorFlow Lite interpreter
To quickly run TensorFlow Lite models with Python, you can install just the TensorFlow Lite interpreter, instead of all TensorFlow packages.
This interpreter-only package is a fraction the size of the full TensorFlow
package and includes the bare minimum code required to run inferences with
TensorFlow Lite—it includes only the
Python class. This small package is ideal when all you want to do is execute
.tflite models and avoid wasting disk space with the large TensorFlow library.
To install, run
pip3 install and pass it the appropriate Python wheel URL from
the following table.
For example, if you have Raspberry Pi that's running Raspbian Buster (which has Python 3.7), install the Python wheel as follows:
pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl
Run an inference using tflite_runtime
To distinguish this interpreter-only package from the full TensorFlow package
(allowing both to be installed, if you choose), the Python module provided in
the above wheel is named
So instead of importing
Interpreter from the
tensorflow module, you need to
import it from
For example, after you install the package above, copy and run the
file. It will (probably) fail because you don't have the
installed. To fix it, edit this line of the file:
import tensorflow as tf
So it instead reads:
import tflite_runtime.interpreter as tflite
And then change this line:
interpreter = tf.lite.Interpreter(model_path=args.model_file)
So it reads:
interpreter = tflite.Interpreter(model_path=args.model_file)
label_image.py again. That's it! You're now executing TensorFlow Lite
For more details about the
Interpreter API, read
Load and run a model in Python.
If you have a Raspberry Pi, try the classify_picamera.py example to perform image classification with the Pi Camera and TensorFlow Lite.
If you're using a Coral ML accelerator, check out the Coral examples on GitHub.
To convert other TensorFlow models to TensorFlow Lite, read about the the TensorFlow Lite Converter.