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MACHINE, THINK!
Buy my e-book Core ML Survival Guide. This guide is a massive collection of tips and tricks for working with Core ML and mlmodel files. For simple tasks Core ML is very easy to use but what do you do when Core ML is giving you trouble? The solution is most likely in this 400+ page book!It contains pretty much everything I learned about Core ML over the past few years. THE “HELLO WORLD” OF NEURAL NETWORKS The “hello world” of neural networks. Neural networks — also known as “deep learning” — are hot! And now iOS 10 and macOS 10.12 come with the BNNS framework, or Basic Neural Network Subroutines, that lets you put neural networks into your own apps. BNNS runs on the CPU and is heavily optimized to be as fast aspossible.
NEW MOBILE NEURAL NETWORK ARCHITECTURES Over the past 18 months or so, a number of new neural network achitectures were proposed specifically for use on mobile and edge devices. It seems that pretty much everyone has figured out now that large models such as VGG16 or ResNet-50 aren’t a good idea on small devices. 😉. I have previously written about MobileNet v1 and v2,and have
ON-DEVICE TRAINING WITH CORE ML Note: Just as a historical note, iPhones and iPads have already supported on-device training since iOS 11.3 was released in late 2017. It just wasn’t very convenient to use. These low-level training facilities are provided by the Metal Performance Shaders framework, which also powers the GPU-accelerated training in Turi Create and Create ML on the Mac. THE LOST ART OF 3D RENDERING WITHOUT SHADERSSEE MORE ONMACHINETHINK.NET
MOBILENET VERSION 2
HOW FAST IS MY MODEL?SEE MORE ON MACHINETHINK.NET CONVOLUTIONAL NEURAL NETWORKS ON THE IPHONE WITH VGGNET The first layers in VGGNet have 64 kernels, the layers at the end have 512. These convolutions are performed one after the other (on the original input data) and the results are stacked in the output of the layer, making this a 3D volume too. For example, the first APPLE’S DEEP LEARNING FRAMEWORKS: BNNS VS. METAL CNN Apple’s deep learning frameworks: BNNS vs. Metal CNN. With iOS 10, Apple introduced two new frameworks for doing deep learning on iOS: BNNS and MPSCNN. BNNS, or bananas Basic Neural Network Subroutines, is part of the Accelerate framework, a collection of math functions that take full advantage of the CPU’s fast vector instructions. MOBILENETV2 + SSDLITE WITH CORE ML pip3 install -U tfcoreml. The conversion process will give us a version of SSD that will work with Core ML but you won’t be able to use it with the new Vision API just yet. Note: The following instructions were tested with coremltools 2.0, tfcoreml 0.3.0, andTensorFlow 1.7.0.
MACHINE, THINK!
Buy my e-book Core ML Survival Guide. This guide is a massive collection of tips and tricks for working with Core ML and mlmodel files. For simple tasks Core ML is very easy to use but what do you do when Core ML is giving you trouble? The solution is most likely in this 400+ page book!It contains pretty much everything I learned about Core ML over the past few years. THE “HELLO WORLD” OF NEURAL NETWORKS The “hello world” of neural networks. Neural networks — also known as “deep learning” — are hot! And now iOS 10 and macOS 10.12 come with the BNNS framework, or Basic Neural Network Subroutines, that lets you put neural networks into your own apps. BNNS runs on the CPU and is heavily optimized to be as fast aspossible.
NEW MOBILE NEURAL NETWORK ARCHITECTURES Over the past 18 months or so, a number of new neural network achitectures were proposed specifically for use on mobile and edge devices. It seems that pretty much everyone has figured out now that large models such as VGG16 or ResNet-50 aren’t a good idea on small devices. 😉. I have previously written about MobileNet v1 and v2,and have
ON-DEVICE TRAINING WITH CORE ML Note: Just as a historical note, iPhones and iPads have already supported on-device training since iOS 11.3 was released in late 2017. It just wasn’t very convenient to use. These low-level training facilities are provided by the Metal Performance Shaders framework, which also powers the GPU-accelerated training in Turi Create and Create ML on the Mac. THE LOST ART OF 3D RENDERING WITHOUT SHADERSSEE MORE ONMACHINETHINK.NET
MOBILENET VERSION 2
HOW FAST IS MY MODEL?SEE MORE ON MACHINETHINK.NET CONVOLUTIONAL NEURAL NETWORKS ON THE IPHONE WITH VGGNET The first layers in VGGNet have 64 kernels, the layers at the end have 512. These convolutions are performed one after the other (on the original input data) and the results are stacked in the output of the layer, making this a 3D volume too. For example, the first APPLE’S DEEP LEARNING FRAMEWORKS: BNNS VS. METAL CNN Apple’s deep learning frameworks: BNNS vs. Metal CNN. With iOS 10, Apple introduced two new frameworks for doing deep learning on iOS: BNNS and MPSCNN. BNNS, or bananas Basic Neural Network Subroutines, is part of the Accelerate framework, a collection of math functions that take full advantage of the CPU’s fast vector instructions. MOBILENETV2 + SSDLITE WITH CORE ML pip3 install -U tfcoreml. The conversion process will give us a version of SSD that will work with Core ML but you won’t be able to use it with the new Vision API just yet. Note: The following instructions were tested with coremltools 2.0, tfcoreml 0.3.0, andTensorFlow 1.7.0.
MACHINE, THINK!
Buy my e-book Core ML Survival Guide. This guide is a massive collection of tips and tricks for working with Core ML and mlmodel files. For simple tasks Core ML is very easy to use but what do you do when Core ML is giving you trouble? The solution is most likely in this 400+ page book!It contains pretty much everything I learned about Core ML over the past few years. FASTER NEURAL NETS FOR IOS AND MACOS Faster neural nets for iOS and macOS. One of the services I provide is converting neural networks to run on iOS devices.. Because neural networks by nature perform a lot of computations, it is important that they run as efficiently as possible on mobile. MACHINE LEARNING CONSULTANCY SERVICES Machine learning consultancy services. Hi, my name is Matthijs Hollemans. I am an independent consultant who specializes in machine learning and AI on mobile and edge devices.. To get a taste for the sort of things I do, check out my blog where I write about the practical aspects of doing ML and AI on smartphones andmicrocontrollers.
NEW MOBILE NEURAL NETWORK ARCHITECTURES Over the past 18 months or so, a number of new neural network achitectures were proposed specifically for use on mobile and edge devices. It seems that pretty much everyone has figured out now that large models such as VGG16 or ResNet-50 aren’t a good idea on smalldevices. 😉
COMPRESSING DEEP NEURAL NETS Like most modern neural networks, MobileNet has many convolutional layers. One way to compress a convolution layer is to sort the weights for that layer from small to large and throw away the connections with the smallest weights. This was the approach used TRAINING ON THE DEVICE Here are some future possibilities that are a bit more advanced: Smart Reply is a model from Google that analyzes an incoming text or email message and suggests an appropriate reply. The model is not currently trained on the device, so it recommends the same kinds of reply to every user, but in theory it could be trained on the user’s own words — which would be better, since not everyone ONE-STAGE OBJECT DETECTION One-stage object detection. Object detection is the computer vision technique for finding objects of interest in an image: This is more advanced than classification, which only tells you what the “main subject” of the image is — whereas object detection can find multiple objects, classify them, and locate where they are in theimage. An
MOBILENETV2 + SSDLITE WITH CORE ML pip3 install -U tfcoreml. The conversion process will give us a version of SSD that will work with Core ML but you won’t be able to use it with the new Vision API just yet. Note: The following instructions were tested with coremltools 2.0, tfcoreml 0.3.0, andTensorFlow 1.7.0.
HELP!? THE OUTPUT OF MY CORE ML MODEL IS WRONG… This is a question that I’ve seen asked multiple times over the past weeks on Stack Overflow, the Apple Developer Forums, and various Slack groups.. Usually it involves Core ML models that take images as input. When you’re using Core ML, it is often not enough to just put your image into a CVPixelBuffer object. Even using Vision to drive Core ML won’t fix this issue. MATRIX MULTIPLICATION WITH METAL PERFORMANCE SHADERS The MPSMatrixMultiplication class is an MPSKernel that computes the following: C = A × B + C. It multiplies matrix A by matrix B but it also adds the previous contents of matrix C, and then overwrites the contents of C with the new results. To be complete, what it computes is this: C = alpha × op (A) × op (B) + beta × C.MACHINE, THINK!
Buy my e-book Core ML Survival Guide. This guide is a massive collection of tips and tricks for working with Core ML and mlmodel files. For simple tasks Core ML is very easy to use but what do you do when Core ML is giving you trouble? The solution is most likely in this 400+ page book!It contains pretty much everything I learned about Core ML over the past few years. FASTER NEURAL NETS FOR IOS AND MACOS Faster neural nets for iOS and macOS. One of the services I provide is converting neural networks to run on iOS devices.. Because neural networks by nature perform a lot of computations, it is important that they run as efficiently as possible on mobile. THE “HELLO WORLD” OF NEURAL NETWORKS Let’s see what happens when we give the neural network some input. If we set the input neurons to in1 = 0 and in2 = 0, then the values of the two hidden neurons h1 and h2 only depend on the biases because the terms with the weights become 0:. h1 = sigmoid(0 * 54 + 0 * 17 - 8) = sigmoid(-8) = 0.000335 h2 = sigmoid(0 * 14 + 0 * 14 - 20) = sigmoid(-20) = 0.000000 MACHINE LEARNING CONSULTANCY SERVICESSEE MORE ON MACHINETHINK.NET HOW FAST IS MY MODEL?SEE MORE ON MACHINETHINK.NETMOBILENET VERSION 2
APPLE’S DEEP LEARNING FRAMEWORKS: BNNS VS. METAL CNN With iOS 10, Apple introduced two new frameworks for doing deep learning on iOS: BNNS and MPSCNN.. BNNS, or bananas Basic Neural Network Subroutines, is part of the Accelerate framework, a collection of math functions that take full advantage of the CPU’s fast vector instructions.. MPSCNN is part of Metal Performance Shaders, a library of optimized compute kernels that run on the GPU instead MOBILENETV2 + SSDLITE WITH CORE ML This blog post is a lightly edited chapter from my book Core ML Survival Guide.. If you’re interested in adding Core ML to your app, or you’re running into trouble getting your model to work, then check out the book.It’s filled with tips and tricks to help you YOLO: CORE ML VERSUS MPSNNGRAPH A few weeks ago I wrote about YOLO, a neural network for object detection.I had implemented that version of YOLO (actually, Tiny YOLO) using Metal Performance Shaders and my Forge neural network library.. Since then Apple has announced two new technologies for doing machine learning on the device: Core ML and the MPS graph API.In this blog post we will implement Tiny YOLO MATRIX MULTIPLICATION WITH METAL PERFORMANCE SHADERSSEE MORE ONMACHINETHINK.NET
MACHINE, THINK!
Buy my e-book Core ML Survival Guide. This guide is a massive collection of tips and tricks for working with Core ML and mlmodel files. For simple tasks Core ML is very easy to use but what do you do when Core ML is giving you trouble? The solution is most likely in this 400+ page book!It contains pretty much everything I learned about Core ML over the past few years. FASTER NEURAL NETS FOR IOS AND MACOS Faster neural nets for iOS and macOS. One of the services I provide is converting neural networks to run on iOS devices.. Because neural networks by nature perform a lot of computations, it is important that they run as efficiently as possible on mobile. THE “HELLO WORLD” OF NEURAL NETWORKS Let’s see what happens when we give the neural network some input. If we set the input neurons to in1 = 0 and in2 = 0, then the values of the two hidden neurons h1 and h2 only depend on the biases because the terms with the weights become 0:. h1 = sigmoid(0 * 54 + 0 * 17 - 8) = sigmoid(-8) = 0.000335 h2 = sigmoid(0 * 14 + 0 * 14 - 20) = sigmoid(-20) = 0.000000 MACHINE LEARNING CONSULTANCY SERVICESSEE MORE ON MACHINETHINK.NET HOW FAST IS MY MODEL?SEE MORE ON MACHINETHINK.NETMOBILENET VERSION 2
APPLE’S DEEP LEARNING FRAMEWORKS: BNNS VS. METAL CNN With iOS 10, Apple introduced two new frameworks for doing deep learning on iOS: BNNS and MPSCNN.. BNNS, or bananas Basic Neural Network Subroutines, is part of the Accelerate framework, a collection of math functions that take full advantage of the CPU’s fast vector instructions.. MPSCNN is part of Metal Performance Shaders, a library of optimized compute kernels that run on the GPU instead MOBILENETV2 + SSDLITE WITH CORE ML This blog post is a lightly edited chapter from my book Core ML Survival Guide.. If you’re interested in adding Core ML to your app, or you’re running into trouble getting your model to work, then check out the book.It’s filled with tips and tricks to help you YOLO: CORE ML VERSUS MPSNNGRAPH A few weeks ago I wrote about YOLO, a neural network for object detection.I had implemented that version of YOLO (actually, Tiny YOLO) using Metal Performance Shaders and my Forge neural network library.. Since then Apple has announced two new technologies for doing machine learning on the device: Core ML and the MPS graph API.In this blog post we will implement Tiny YOLO MATRIX MULTIPLICATION WITH METAL PERFORMANCE SHADERSSEE MORE ONMACHINETHINK.NET
MACHINE, THINK!
Buy my e-book Core ML Survival Guide. This guide is a massive collection of tips and tricks for working with Core ML and mlmodel files. For simple tasks Core ML is very easy to use but what do you do when Core ML is giving you trouble? The solution is most likely in this 400+ page book!It contains pretty much everything I learned about Core ML over the past few years. FASTER NEURAL NETS FOR IOS AND MACOS Faster neural nets for iOS and macOS. One of the services I provide is converting neural networks to run on iOS devices.. Because neural networks by nature perform a lot of computations, it is important that they run as efficiently as possible on mobile. NEW MOBILE NEURAL NETWORK ARCHITECTURES Over the past 18 months or so, a number of new neural network achitectures were proposed specifically for use on mobile and edge devices. It seems that pretty much everyone has figured out now that large models such as VGG16 or ResNet-50 aren’t a good idea on smalldevices. 😉
ONE-STAGE OBJECT DETECTION Object detection is the computer vision technique for finding objects of interest in an image: This is more advanced than classification, which only tells you what the “main subject” of the image is — whereas object detection can find multiple objects, classify them, and locate where they are in the image.. An object detection model predicts bounding boxes, one for each object it finds TRAINING ON THE DEVICE Here are some future possibilities that are a bit more advanced: Smart Reply is a model from Google that analyzes an incoming text or email message and suggests an appropriate reply. The model is not currently trained on the device, so it recommends the same kinds of reply to every user, but in theory it could be trained on the user’s own words — which would be better, since not everyone UPSAMPLING IN CORE ML Resizing feature maps is a common operation in many neural networks, especially those that perform some kind of image segmentation task. One issue I ran into recently while converting a neural network to Core ML, is that the original PyTorch model gave different results for its bilinear upsampling than Core ML, and I wanted to understand why.. When converting models between deep MATRIX MULTIPLICATION WITH METAL PERFORMANCE SHADERS Note: The formula only gives a theoretical upper limit on the number of operations. It’s very likely that these matrix multiplication routines use lots of optimizations under the hood. Case in point: multiplying two square 1500×1500 matrices is over 6 billion operations but BLAS is faster here than MPS, so perhaps multiplying square matrices is a special case BLAS has extra optimizations for. ON-DEVICE TRAINING WITH CORE ML Note: You might also be able to use the Core ML converters to make your models updatable. Currently, only the Keras converter supports this. With the option respect_trainable=True, any layers that were trainable in the original model also become trainable in the Core ML model.(Unfortunately, the Keras converter only does half a job and you still need to add the training MOBILENETV2 + SSDLITE WITH CORE ML The ssdlite_mobilenet_v2_coco download contains the trained SSD model in a few different formats: a frozen graph, a checkpoint, and a SavedModel. tfcoreml needs to use a frozen graph but the downloaded one gives errors — it contains “cycles” or loops, which are ano-go for tfcoreml.
HELP!? THE OUTPUT OF MY CORE ML MODEL IS WRONG… This is a question that I’ve seen asked multiple times over the past weeks on Stack Overflow, the Apple Developer Forums, and various Slack groups.. Usually it involves Core ML models that take images as input. When you’re using Core ML, it is often not enough to just put your image into a CVPixelBuffer object. Even using Vision to drive Core ML won’t fix this issue. Blog Software Hire MeYOUR APP +
DEEP LEARNING
DEEP LEARNING IS BREAKING ALL THE RECORDS IN COMPUTER VISION, NATURAL LANGUAGE PROCESSING, SPEECH RECOGNITION, ROBOTICS, MEDICINE, BIOLOGY, AND MANY OTHER FIELDS. AND NOW IT RUNS ON MOBILE… WHAT CAN DEEP LEARNING DO _FOR YOU?_ Mobile phones and tablets are now POWERFUL ENOUGH to take advantage of the awesome opportunities offered by machine learning and artificialintelligence.
Here are some of the things your app can do with DEEP LEARNING:COMPUTER VISION
* detect objects, faces & landmarks on photos, videos, or livecamera
* recognize handwriting and printed text within images * search using pictures * track motion and poses * recognize gestures * understand emotional cues in photos and videos * enhance images and remove imperfections * automatically tag and categorize visual content * add special effects and filters * detect explicit content * create 3D models of interior spaces* augmented reality
NATURAL LANGUAGE PROCESSING * extract meaning from text * chatbots and conversational UI * translate text into other languages * intelligent spelling correction* summarize texts
* detect topics and sentimentSPEECH AND AUDIO
* speech recognition and synthesis * translate speech in real-time * automatic subtitling of videos * recommend music based on your mood or surroundings * intelligently filtering audio * sound / music recognition * language or dialect detection * identify speakers by voice AND MUCH MORE… There’s no limit to what’s possible when you combine the raw power of deep learning with the phone’s camera and its many other sensors. Deep learning turns your mobile into a TRULY SMART ASSISTANT. DEEP LEARNING ON THE DEVICE Companies such as Google, Amazon, and Microsoft offer deep learning services in the cloud — but there are many advantages to doing deep learning DIRECTLY ON THE USER’S DEVICE, for a more seamless user experience. Getting deep learning to work well on mobile comes with its own set ofchallenges:
* IT NEEDS TO BE FAST — which means using optimized compute kernels on the GPU. Building and deploying those compute kernels takesspecial skills.
* The model needs to be small enough to FIT IN MEMORY while still providing great performance. Compressing models to remove unnecessary parts is as much an art as a science. * Training on the device is currently impractical for most applications. To keep the model up-to-date, you need to have some way to RETRAIN YOUR MODEL and then distribute the new weights to theuser’s device.
* To train on user data, you need some way to COLLECT DATA from their devices and gather it in the cloud — without violating the user’s privacy. Doing this right requires care and consideration. _Making deep learning models work well on iOS devices is what I specialize in. LET’S TALK if you need advice on how to add deep learning to your app._ LEARN MORE about my services IOS MACHINE LEARNING BOOKS Machine Learning by Tutorials explains how to get started with machine learning for people who are already familiar with iOS development. Core ML Survival Guide is for developers who are running into problems getting their models to work with Core ML — or who want to do advanced things that are not well documented elsewhere. MOBILENET SOURCE CODE LIBRARY The _MobileNet_ neural network architecture is designed to run efficiently on mobile devices. It’s a fast, accurate, and powerful feature extractor. I recommend using it over larger and slower architectures such as VGG-16, ResNet, and Inception. Because MobileNet-based models are becoming ever more popular, I’ve created a source code library for iOS and macOS that has Metal-accelerated implementations of MOBILENET V1 ANDV2.
This library makes it easy to add MobileNet-based neural networks into your apps, for tasks such as: * image classification * real-time object detection — with SSD or SSDLITE * semantic image segmentation — with DEEPLABV3+ * as a feature extractor that is part of a custom model Because this library is written to take advantage of Metal, it is MUCH FASTER THAN CORE ML and TensorFlow Lite! If you’re interested in using MobileNet in your app or as the backbone for a larger model, this library is the best way to get started. Click to learn moreWHO AM I?
MATTHIJS HOLLEMANS
Programming is the most fun for me when it’s difficult. That’s why I specialize in low-level coding, GPU optimization, and algorithms. My current research interest is DEEP LEARNING ON IOS, and I’d love to help you add deep learning into your app. Send me an emailRECENT BLOG POSTS
New mobile neural network architectures8 Apr 2020
Upsampling in Core ML6 Feb 2020
How to convert images to MLMultiArray9 Dec 2019
On-device training with Core ML – part 41 Dec 2019
Core ML and Combine
13 Nov 2019
On-device training with Core ML – part 314 Sep 2019
On-device training with Core ML – part 210 Aug 2019
On-device training with Core ML – part 119 Jul 2019
An in-depth look at Core ML 38 Jun 2019
MobileNetV2 + SSDLite with Core ML17 Dec 2018
New Machine Learning Books for iOS5 Dec 2018
Why learn algorithms?16 Aug 2018
How fast is my model?30 Jun 2018
One-stage object detection9 Jun 2018
MobileNet version 2 22Apr 2018
Custom Layers in Core ML11 Dec 2017
Training on the device22 Nov 2017
Compressing deep neural nets2 Sep
2017
A peek inside Core ML21 Aug 2017
Help!? The output of my Core ML model is wrong…26
Jul 2017
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