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Android Developers Guide to Machine Learning With MLKit, Tensorflow & Firebase

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Rebecca Franks @riggaroo Google Developer Expert Android @ Over Pluralsight Author GDG Johannesburg Organiser

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Who considers themselves an expert in Machine Learning?

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What is Machine Learning?

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Machine learning is an application of Artificial Intelligence in which we input a lot of data and let the machines learn “by themselves”

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Face detection$% W orks offline

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Demo

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⛩ Landmark detection

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Demo

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Image Labelling W orks offline

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Barcode scanning W orks offline

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val options = FirebaseVisionBarcodeDetectorOptions.Builder() .setBarcodeFormats(FirebaseVisionBarcode.FORMAT_QR_CODE) .build() val image = FirebaseVisionImage.fromBitmap(bitmap) val detector = FirebaseVision.getInstance() .getVisionBarcodeDetector(options) detector.detectInImage(image) .addOnSuccessListener { processedBitmap.postValue(barcodeProcessor.drawBoxes(bitmap, it))

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val options = FirebaseVisionBarcodeDetectorOptions.Builder() .setBarcodeFormats(FirebaseVisionBarcode.FORMAT_QR_CODE) .build() val image = FirebaseVisionImage.fromBitmap(bitmap) val detector = FirebaseVision.getInstance() .getVisionBarcodeDetector(options) detector.detectInImage(image) .addOnSuccessListener { processedBitmap.postValue(barcodeProcessor.drawBoxes(bitmap, it)) var result = String() it.forEach { result += "VALUE TYPE: ${it.valueType} Raw Value: ${it.rawValue}" textResult.postValue(result)

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val image = FirebaseVisionImage.fromBitmap(bitmap) val detector = FirebaseVision.getInstance() .getVisionBarcodeDetector(options) detector.detectInImage(image) .addOnSuccessListener { processedBitmap.postValue(barcodeProcessor.drawBoxes(bitmap, it)) var result = String() it.forEach { result += "VALUE TYPE: ${it.valueType} Raw Value: ${it.rawValue}" textResult.postValue(result) } }.addOnFailureListener{ textResult.postValue(it.message) }

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OCR W orks offline

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On Device vs Cloud

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private fun doOcrDetection(bitmap: Bitmap){ val detector = FirebaseVision.getInstance() .visionTextDetector val firebaseImage = FirebaseVisionImage.fromBitmap(bitmap) detector.detectInImage(firebaseImage) .addOnSuccessListener { processedBitmap.postValue(ocrProcessor.drawBoxes(bitmap, it)) var result = String() it.blocks.forEach { result += " " + it.text textResult.postValue(result)

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private fun doOcrDetection(bitmap: Bitmap){ val detector = FirebaseVision.getInstance() .visionTextDetector val firebaseImage = FirebaseVisionImage.fromBitmap(bitmap) detector.detectInImage(firebaseImage) .addOnSuccessListener { processedBitmap.postValue(ocrProcessor.drawBoxes(bitmap, it)) var result = String() it.blocks.forEach { result += " " + it.text textResult.postValue(result) } } .addOnFailureListener{ Toast.makeText(/../“Error detecting Text $it”/../) }

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private fun doOcrDetection(bitmap: Bitmap){ val detector = FirebaseVision.getInstance() .visionTextDetector val firebaseImage = FirebaseVisionImage.fromBitmap(bitmap) detector.detectInImage(firebaseImage) .addOnSuccessListener { processedBitmap.postValue(ocrProcessor.drawBoxes(bitmap, it)) var result = String() it.blocks.forEach { result += " " + it.text textResult.postValue(result) } } .addOnFailureListener{ Toast.makeText(/../“Error detecting Text $it”/../) }

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Custom Tensorflow Models W orks offline

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TensorFlow

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Retrain existing model 0 mobilenet_v1

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What if I could tell what kind of chips I was eating?

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Gather Training Data FFMPEG Folders of Images Retrain with new images Optimize for mobile Embed in app Store in Firebase App uses model

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Gather training data

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Export to Images using ffmpeg ffmpeg -i flings.mp4 flings/flings_%04d.jpg

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Folders of images

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Retrain with new images python -m scripts.retrain \ --bottleneck_dir=tf_files/bottlenecks \ --how_many_training_steps=500 \ --model_dir=tf_files/models/ \ --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \ --output_graph=tf_files/retrained_graph.pb \ --output_labels=tf_files/retrained_labels.txt \ --architecture="${ARCHITECTURE}" \ --image_dir=training_data/south_african_chips

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Optimize for mobile bazel-bin/tensorflow/contrib/lite/toco/toco \ --input_file=AgencyDay/retrained_graph.pb \ --output_file=AgencyDay/chips_optimized_graph.tflite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --input_shape=1,${IMAGE_SIZE},${IMAGE_SIZE},3 \ --input_array=input \ --output_array=final_result \ --inference_type=FLOAT \ --input_data_type=FLOAT

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Insert into App https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/ https://github.com/googlecodelabs/tensorflow-for-poets-2

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Nik Nak 1 Or Not 2 bit.ly/mlkit-riggaroo

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What if our model changes?

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Ship an app update 3 And hope that people download it 4

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Host on Firebase Updates automatically downloaded

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val cloudSource = FirebaseCloudModelSource.Builder("my_cloud_model") .enableModelUpdates(true) .setInitialDownloadConditions(conditions) .setUpdatesDownloadConditions(conditions) .build() FirebaseModelManager.getInstance() .registerCloudModelSource(cloudSource) ……

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g.co/codelabs/mlkit-android-custom-model

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You don’t need to be a ML Expert to take advantage of ML in your apps!

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Thank you!

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Resources - https://codelabs.developers.google.com/ codelabs/tensorflow-for-poets - https://codelabs.developers.google.com/ codelabs/tensorflow-for-poets-2-tflite/ - https://codelabs.developers.google.com/ codelabs/mlkit-android-custom-model/ #0 - https://github.com/riggaroo/android- demo-mlkit