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This code makes use of tensorflow pb file converted to tflite
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Tensorflow pb file converted to tflite:
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz | tar xzv -C /tmp
tflite_convert --output_file=/tmp/foo.tflite --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb --input_arrays=input --output_arrays=MobilenetV1/Predictions/Reshape_1
- Convert FR pb model to tflite: https://www.tensorflow.org/lite/convert/cmdline#usage
pip install tf-nightly
tflite_convert --output_file=gvFR.tflite --graph_def_file=09-02_02-45.pb --input_arrays=input --output_arrays=embedding --input_shapes=10,224,224,3 --enable_v1_converter
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Implement of Android NDK to load custom Face Recognition tflite models instead of the classifier example from Google TensorFlow example
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Images below shows face extract 512-D features and similarity between Face One and Two.
- This is an example project for integrating TensorFlow Lite into Android application
- This project include an example for object detection for an image taken from camera using TensorFlow Lite library.
- Support it by clicking the ⭐ button on the upper right of this page. ✌️
- The classifier example has been taken from Google TensorFlow example.
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Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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