Tensorflow Lite on Raspberry Pi - Installation

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tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl is not a supported wheel on this platform.

In my current project I'm using machine learning on the Raspberry Pi for sensor fusion. Since I heard about the release of Tensorflow Lite I'm really interested to deploy and use it to run Lite models on the platform.

On the Tensorflow website are hints for Android and iOS, but I couldn't find any hints about any other platforms. Is there a (WIP) installation/compile guide out to bring TF Lite to the Raspi?


@all, if you are still in the trials to make tensorflow lite running on Raspberry Pi 3, my "pull-request" may be useful. Please look at https://github.com/tensorflow/tensorflow/pull/24194.

Following the steps, 2 apps (label_image and camera) can be running on Raspberry Pi 3.



Build TensorFlow Lite for Raspberry Pi, To quickly run TensorFlow Lite models with Python, you can install just the For example, if you have Raspberry Pi that's running Raspbian  Once all the files are placed in a folder (we used ~/software/tensorflow_lite) on your Raspberry Pi, s tart Code::Blocks and load the TestTensorFlow_Lite.cbp project file. Or simply double click on the TestTensorFlow_Lite .cbp in your File Manager.

There is a very small section on Raspberry PI in the TFLite docs at https://www.tensorflow.org/mobile/tflite/devguide#raspberry_pi. That section links to this GitHub doc with instructions for building TFLite on Raspberry PI - tensorflow/rpi.md.

There is no official demo app yet, but the first location says one is planned. It will probably be shared at that same location when ready (that is where the Android and iOS demo apps are described).

Python quickstart, As an added bonus, you can hook up a pair of headphones or a speaker to the Raspberry Pi and it will actually tell you what it is detecting. Make sure you don't  Google TensorFlow 1.9 officially supports the Raspberry Pi, making it possible to quickly install TensorFlow and start learning AI techniques with a Raspberry Pi. Back in The MagPi issue 71 we noted that it was getting easier to install TensorFlow on a Raspberry Pi. This latest news makes installing TensorFlow 1.9 as simple as using pip.

You can install TensorFlow PIP on Raspberry pi with "pip install tensorflow" however, if you want only TFLite you can build a smaller pip that has only the tflite interpreter (you can then do conversion on another big machine).

Info on how to do it is here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/pip_package

Then, you can use it. Here is an example of how you might use it!

import tflite_runtime as tflr

interpreter = tflr.lite.Interpreter(model_path="mobilenet_float.tflite")
input = interpreter.get_input_details()[0]
output = interpreter.get_input_details()[0]

cap = cv2.VideoCapture(0)  # open 0th web camera

while 1:
  ret, frame = cap.read()
  frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
  frame = cv2.resize(frame, input.shape[2],input.shape[1])
  frame = np.reshape(im, input.shape).astype(np.float32)/128.0-1.0
  interpreter.set_tensor(input["index"], frame)
  labels = interpreter.get_tensor(output["index"])
  top_label_index = np.argmax(labels, axis=-1)

Hope this helps.

TensorFlow Lite 2.0 Setup, I've spent the last 2 hours trying to figure out how to install tensorflow lite on the Raspberry Pi. Apparently it's possible, but not without jumping  You can install TensorFlow PIP on Raspberry pi with "pip install tensorflow" however, if you want only TFLite you can build a smaller pip that has only the tflite interpreter (you can then do conversion on another big machine).

I would suggest next links:

  1. The lightest way is by using TensorFlowLite interpreter only. You can find more information following this link: Install just the TensorFlow Lite interpreter

You have to remember, if you use interpreter only you have to follow a little bit different logic.

# Initiate the interpreter
interpreter = tf.lite.Interpreter(PATH_TO_SAVED_TFLITE_MODEL)

# Allocate memory for tensors

# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Add a batch dimension if needed (data_tensor - your data input)
input_data = tf.extend.dims(data_tensor, axis=0)

# Predict
interpreter.set_tensor(input_details[0]['index'], data_tensor)

# Obtain results
predictions = interpreter.get_tensor(output_details[0]['index'])
  1. Build from source for the Raspberry Pi

  2. Install TensorFlow with pip

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