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diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java
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+++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java
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+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+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
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import java.io.IOException;
+
+/** This classifier works with the quantized MobileNet model. */
+public class ImageClassifierQuantizedMobileNet extends ImageClassifier {
+
+  /**
+   * An array to hold inference results, to be feed into Tensorflow Lite as outputs. This isn't part
+   * of the super class, because we need a primitive array here.
+   */
+  private byte[][] labelProbArray = null;
+
+  /**
+   * Initializes an {@code ImageClassifier}.
+   *
+   * @param activity
+   */
+  ImageClassifierQuantizedMobileNet(Activity activity) throws IOException {
+    super(activity);
+    labelProbArray = new byte[1][getNumLabels()];
+  }
+
+  @Override
+  protected String getModelPath() {
+    // you can download this file from
+    // https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip
+    return "mobilenet_quant_v1_224.tflite";
+  }
+
+  @Override
+  protected String getLabelPath() {
+    return "labels_mobilenet_quant_v1_224.txt";
+  }
+
+  @Override
+  protected int getImageSizeX() {
+    return 224;
+  }
+
+  @Override
+  protected int getImageSizeY() {
+    return 224;
+  }
+
+  @Override
+  protected int getNumBytesPerChannel() {
+    // the quantized model uses a single byte only
+    return 1;
+  }
+
+  @Override
+  protected void addPixelValue(int pixelValue) {
+    imgData.put((byte) ((pixelValue >> 16) & 0xFF));
+    imgData.put((byte) ((pixelValue >> 8) & 0xFF));
+    imgData.put((byte) (pixelValue & 0xFF));
+  }
+
+  @Override
+  protected float getProbability(int labelIndex) {
+    return labelProbArray[0][labelIndex];
+  }
+
+  @Override
+  protected void setProbability(int labelIndex, Number value) {
+    labelProbArray[0][labelIndex] = value.byteValue();
+  }
+
+  @Override
+  protected float getNormalizedProbability(int labelIndex) {
+    return (labelProbArray[0][labelIndex] & 0xff) / 255.0f;
+  }
+
+  @Override
+  protected void runInference() {
+    tflite.run(imgData, labelProbArray);
+  }
+}