The ML Kit for Firebase Android Quickstart app demonstrates how to use the various features of ML Kit to add machine learning to your application.

• Read more about ML Kit for Firebase

• Add Firebase to your Android Project.
• Run the sample on an Android device.
• Choose LivePreviewActivity to see a demo of the following APIs:
  • Face detection
  • Text recognition (on-device)
  • Barcode scanning
  • Image labeling (on-device)
  • Landmark recognition
  • Custom model (Labeled "Classification"). The custom model used in this sample, MobileNet_v1, is already included as a local asset in the project. To use this sample with a hosted model, follow the directions under the "Hosting a Custom Model" section of this readme.
• Choose StillImageActivity to see a demo of the following:
  • Image labeling (Cloud)
  • Landmark recognition (Cloud)
  • Text recognition (Cloud)
  • Document text recognition (Cloud)

• Download the TensorFlow Lite custom model we are using in this sample.
• Go to the Firebase console.
• Select your project.
• Select ML Kit under the DEVELOP section in the left hand navigation.
• Click on the CUSTOM tab.
• Click on Add another model and use "mobilenet_v1" as the name.
• Click BROWSE and upload the mobilenet_v1_1.0_224_quant.tflite file you downloaded earlier.
• Click PUBLISH.

• Stack Overflow
• Firebase Support.

Copyright 2018 Google, Inc.

Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you 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

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