Brain  Crist

Brain Crist

1596973140

Working with SnapML Templates in Lens Studio: An Overview

Image for post

Just last month (June 2020) Snapchat released a major update for its Lens creation software — Lens Studio 3.0. Out of all the new things that came along with the release, one feature that stood apart was SnapML.

SnapML allows lens creators to use their own custom machine learning models inside filters. Snapchat filters already use ML for a variety of things like face tracking, surface tracking, segmentation, facial gestures, and more.

But with SnapML, developers can now go beyond those limits, using the breadth of their imaginations to combine custom ML models with Snapchat’s feature-rich augmented reality tools—and an audience of millions!

Lens Studio 3.0 introduces SnapML for adding custom neural networks directly to Snapchat

Including a conversation with Hart Woolery, CEO of 2020CV and a SnapML creator

heartbeat.fritz.ai

Getting started with SnapML in Lens Studio

Snapchat has made a lot of initial efforts to make the experience of implementing ML models as smooth and straightforward as possible. To do this, they’ve provided several ready-to-use templates that could be turned into AR experiences in only a few minutes. Let’s explore these templates and learn how to use them to create filters.


Lens Studio Templates

There are 5 main Lens Studio templates provided by Snap that cover some of the most common mobile machine learning tasks. We’ll cover the ML side of things a bit later, but first, here’s a look at the template projects inside Lens Studio:

Style Transfer

Style transfer is used to modify the overall visual appearance of the camera texture. Essentially, this task allows us to recompose the content of one image in the style of another.

Image for post

This could be used to create unique artistic styles or color filters. Imagine a selfie in the style of your favorite visual artists. This is similar to what we see in many image and video editing apps like Prisma or Looq.

This project template contains an example ML model with a style transfer visual effect. We’ll discuss in more detail below how to implement your own custom style transfer model.

Classification

This template uses the image classification ML technique, which is used to classify images into different labels. This task is also sometimes referred to as image labeling or image recognition.

Image for post

Image Source

This could be used to tell, for example, whether user is wearing a hat or not in a given image or video frame. However, to create this kind of model, you’ll need to train it on a large dataset. There’s a wide variety of image classification datasets out there, but if there’s no dataset available for your use case, you’ll need to collect and label one.

This template contains an example ML model that can tell if the user is wearing glasses or not, showing a particle effect if true. We’ll discuss in more detail below how to implement your own custom image classification model.

Object Detection

The object detection template could be used to identify and locate one or more target objects in a given image or video frame. Object detection is somewhat similar to image classification, but it’s important to quickly distinguish the two:

  • Image classification assigns a label to an image. A picture of a dog receives the label “dog”. A picture of two dogs, still receives the label “dog”
  • Object detection, on the other hand, draws what’s called a bounding box around each dog and labels the box “dog”. The model predicts where each object is and what label should be applied. In that way, object detection provides more information about an image than recognition.

Image for post

In Lens Studio, you could use this template to identify the location of the ring finger in an image or video frame, and use that prediction to place an AR ring for virtual product try-ons.

Again, here you’ll need a large dataset of objects you want to detect—and their bounding box coordinates—to create a custom model for this effect. Here are some available datasets to consider working with.

This template contains an example ML model that recognizes cars from the COCO dataset and draws bounding boxes around them. We’ll discuss in more detail below how to implement your own custom object detection model.

Custom Segmentation

Segmentation is an ML technique used to differentiate between the specific parts or elements of an image. Specifically, it works at a pixel-level—the goal is to assign each pixel in an image to the object to which it belongs (i.e. class label).

Image for post

Segmentation could be used to add effects to a given segmented region. Snap Lense already had built-in segmentation to separate the user from the background, but with custom ML models you extend this task to anything you can think of—for example, segmenting the sky and changing it to into a beautiful, sunset-like pink-orange color.

This template contains an example ML model that recognizes a pizza and adds a sizzling effect to it. We’ll discuss in more detail below how to implement your own custom segmentation model.

#heartbeat #mobile-app-development #machine-learning #snapchat #lens-studio

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Working with SnapML Templates in Lens Studio: An Overview
Brain  Crist

Brain Crist

1596973140

Working with SnapML Templates in Lens Studio: An Overview

Image for post

Just last month (June 2020) Snapchat released a major update for its Lens creation software — Lens Studio 3.0. Out of all the new things that came along with the release, one feature that stood apart was SnapML.

SnapML allows lens creators to use their own custom machine learning models inside filters. Snapchat filters already use ML for a variety of things like face tracking, surface tracking, segmentation, facial gestures, and more.

But with SnapML, developers can now go beyond those limits, using the breadth of their imaginations to combine custom ML models with Snapchat’s feature-rich augmented reality tools—and an audience of millions!

Lens Studio 3.0 introduces SnapML for adding custom neural networks directly to Snapchat

Including a conversation with Hart Woolery, CEO of 2020CV and a SnapML creator

heartbeat.fritz.ai

Getting started with SnapML in Lens Studio

Snapchat has made a lot of initial efforts to make the experience of implementing ML models as smooth and straightforward as possible. To do this, they’ve provided several ready-to-use templates that could be turned into AR experiences in only a few minutes. Let’s explore these templates and learn how to use them to create filters.


Lens Studio Templates

There are 5 main Lens Studio templates provided by Snap that cover some of the most common mobile machine learning tasks. We’ll cover the ML side of things a bit later, but first, here’s a look at the template projects inside Lens Studio:

Style Transfer

Style transfer is used to modify the overall visual appearance of the camera texture. Essentially, this task allows us to recompose the content of one image in the style of another.

Image for post

This could be used to create unique artistic styles or color filters. Imagine a selfie in the style of your favorite visual artists. This is similar to what we see in many image and video editing apps like Prisma or Looq.

This project template contains an example ML model with a style transfer visual effect. We’ll discuss in more detail below how to implement your own custom style transfer model.

Classification

This template uses the image classification ML technique, which is used to classify images into different labels. This task is also sometimes referred to as image labeling or image recognition.

Image for post

Image Source

This could be used to tell, for example, whether user is wearing a hat or not in a given image or video frame. However, to create this kind of model, you’ll need to train it on a large dataset. There’s a wide variety of image classification datasets out there, but if there’s no dataset available for your use case, you’ll need to collect and label one.

This template contains an example ML model that can tell if the user is wearing glasses or not, showing a particle effect if true. We’ll discuss in more detail below how to implement your own custom image classification model.

Object Detection

The object detection template could be used to identify and locate one or more target objects in a given image or video frame. Object detection is somewhat similar to image classification, but it’s important to quickly distinguish the two:

  • Image classification assigns a label to an image. A picture of a dog receives the label “dog”. A picture of two dogs, still receives the label “dog”
  • Object detection, on the other hand, draws what’s called a bounding box around each dog and labels the box “dog”. The model predicts where each object is and what label should be applied. In that way, object detection provides more information about an image than recognition.

Image for post

In Lens Studio, you could use this template to identify the location of the ring finger in an image or video frame, and use that prediction to place an AR ring for virtual product try-ons.

Again, here you’ll need a large dataset of objects you want to detect—and their bounding box coordinates—to create a custom model for this effect. Here are some available datasets to consider working with.

This template contains an example ML model that recognizes cars from the COCO dataset and draws bounding boxes around them. We’ll discuss in more detail below how to implement your own custom object detection model.

Custom Segmentation

Segmentation is an ML technique used to differentiate between the specific parts or elements of an image. Specifically, it works at a pixel-level—the goal is to assign each pixel in an image to the object to which it belongs (i.e. class label).

Image for post

Segmentation could be used to add effects to a given segmented region. Snap Lense already had built-in segmentation to separate the user from the background, but with custom ML models you extend this task to anything you can think of—for example, segmenting the sky and changing it to into a beautiful, sunset-like pink-orange color.

This template contains an example ML model that recognizes a pizza and adds a sizzling effect to it. We’ll discuss in more detail below how to implement your own custom segmentation model.

#heartbeat #mobile-app-development #machine-learning #snapchat #lens-studio

Brain  Crist

Brain Crist

1596976800

Working with SnapML Templates in Lens Studio: An Overview

HTML to Markdown

Image for post

Just last month (June 2020) Snapchat released a major update for its Lens creation software — Lens Studio 3.0. Out of all the new things that came along with the release, one feature that stood apart was SnapML.

SnapML allows lens creators to use their own custom machine learning models inside filters. Snapchat filters already use ML for a variety of things like face tracking, surface tracking, segmentation, facial gestures, and more.

But with SnapML, developers can now go beyond those limits, using the breadth of their imaginations to combine custom ML models with Snapchat’s feature-rich augmented reality tools—and an audience of millions!

Lens Studio 3.0 introduces SnapML for adding custom neural networks directly to Snapchat

Including a conversation with Hart Woolery, CEO of 2020CV and a SnapML creator

heartbeat.fritz.ai

Getting started with SnapML in Lens Studio

Snapchat has made a lot of initial efforts to make the experience of implementing ML models as smooth and straightforward as possible. To do this, they’ve provided several ready-to-use templates that could be turned into AR experiences in only a few minutes. Let’s explore these templates and learn how to use them to create filters.


Lens Studio Templates

There are 5 main Lens Studio templates provided by Snap that cover some of the most common mobile machine learning tasks. We’ll cover the ML side of things a bit later, but first, here’s a look at the template projects inside Lens Studio:

Style Transfer

Style transfer is used to modify the overall visual appearance of the camera texture. Essentially, this task allows us to recompose the content of one image in the style of another.

Image for post

This could be used to create unique artistic styles or color filters. Imagine a selfie in the style of your favorite visual artists. This is similar to what we see in many image and video editing apps like Prisma or Looq.

This project template contains an example ML model with a style transfer visual effect. We’ll discuss in more detail below how to implement your own custom style transfer model.

Classification

This template uses the image classification ML technique, which is used to classify images into different labels. This task is also sometimes referred to as image labeling or image recognition.

Image for post

Image Source

This could be used to tell, for example, whether user is wearing a hat or not in a given image or video frame. However, to create this kind of model, you’ll need to train it on a large dataset. There’s a wide variety of image classification datasets out there, but if there’s no dataset available for your use case, you’ll need to collect and label one.

This template contains an example ML model that can tell if the user is wearing glasses or not, showing a particle effect if true. We’ll discuss in more detail below how to implement your own custom image classification model.

Object Detection

The object detection template could be used to identify and locate one or more target objects in a given image or video frame. Object detection is somewhat similar to image classification, but it’s important to quickly distinguish the two:

  • Image classification assigns a label to an image. A picture of a dog receives the label “dog”. A picture of two dogs, still receives the label “dog”
  • Object detection, on the other hand, draws what’s called a bounding box around each dog and labels the box “dog”. The model predicts where each object is and what label should be applied. In that way, object detection provides more information about an image than recognition.

Image for post

In Lens Studio, you could use this template to identify the location of the ring finger in an image or video frame, and use that prediction to place an AR ring for virtual product try-ons.

Again, here you’ll need a large dataset of objects you want to detect—and their bounding box coordinates—to create a custom model for this effect. Here are some available datasets to consider working with.

This template contains an example ML model that recognizes cars from the COCO dataset and draws bounding boxes around them. We’ll discuss in more detail below how to implement your own custom object detection model.

Custom Segmentation

Segmentation is an ML technique used to differentiate between the specific parts or elements of an image. Specifically, it works at a pixel-level—the goal is to assign each pixel in an image to the object to which it belongs (i.e. class label).

Image for post

Segmentation could be used to add effects to a given segmented region. Snap Lense already had built-in segmentation to separate the user from the background, but with custom ML models you extend this task to anything you can think of—for example, segmenting the sky and changing it to into a beautiful, sunset-like pink-orange color.

This template contains an example ML model that recognizes a pizza and adds a sizzling effect to it. We’ll discuss in more detail below how to implement your own custom segmentation model.
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This project template contains an example ML model with a style transfer visual effect. We’ll discuss in more detail below how to implement your own custom style transfer model.
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Classification

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This template uses the image classification ML technique, which is used to classify images into different labels. This task is also sometimes referred to as image labeling or image recognition.
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Image Source
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This could be used to tell, for example, whether user is wearing a hat or not in a given image or video frame. However, to create this kind of model, you’ll need to train it on a large dataset. There’s a wide variety of image classification datasets out there, but if there’s no dataset available for your use case, you’ll need to collect and label one.
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This template contains an example ML model that can tell if the user is wearing glasses or not, showing a particle effect if true. We’ll discuss in more detail below how to implement your own custom image classification model.
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Object Detection

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The object detection template could be used to identify and locate one or more target objects in a given image or video frame. Object detection is somewhat similar to image classification, but it’s important to quickly distinguish the two:
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  • Image classification assigns a label to an image. A picture of a dog receives the label “dog”. A picture of two dogs, still receives the label “dog”
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  • Object detection, on the other hand, draws what’s called a bounding box around each dog and labels the box “dog”. The model predicts where each object is and what label should be applied. In that way, object detection provides more information about an image than recognition.
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    Image for post
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    In Lens Studio, you could use this template to identify the location of the ring finger in an image or video frame, and use that prediction to place an AR ring for virtual product try-ons.
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    Again, here you’ll need a large dataset of objects you want to detect—and their bounding box coordinates—to create a custom model for this effect. Here are some available datasets to consider working with.
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    This template contains an example ML model that recognizes cars from the COCO dataset and draws bounding boxes around them. We’ll discuss in more detail below how to implement your own custom object detection model.
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Custom Segmentation

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Segmentation is an ML technique used to differentiate between the specific parts or elements of an image. Specifically, it works at a pixel-level—the goal is to assign each pixel in an image to the object to which it belongs (i.e. class label).
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Image for post
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Segmentation could be used to add effects to a given segmented region. Snap Lense already had built-in segmentation to separate the user from the background, but with custom ML models you extend this task to anything you can think of—for example, segmenting the sky and changing it to into a beautiful, sunset-like pink-orange color.
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This template contains an example ML model that recognizes a pizza and adds a sizzling effect to it. We’ll discuss in more detail below how to implement your own custom segmentation model.

#heartbeat #mobile-app-development #machine-learning #snapchat #lens-studio

Rusty  Shanahan

Rusty Shanahan

1596191040

Working with SnapML Templates in Lens Studio: Style Transfer

Artistic style transfer is one of the most intuitive and accessible computer vision tasks out there. Though there’s a lot happening under the hood of a style transfer model, functionally, it’s quite simple.

Style transfer takes two images — a content image and a style reference image — and blends them so that the resulting output image retains the core elements of the content image, but appears to be “painted” in the style of the style reference image.

_For a more complete look at what style transfer is, how it works, and what it’s being used for, _check out our full guide.

Style transfer models, as it turns out, also run very well on mobile phones, for both images and real-time video. As such, it’s not entirely surprising that Snap’s Lens Studio 3.0, with the introduction of SnapML, includes a template for building Lenses using style transfer models.

So far, we’ve covered the release of SnapML, taken a closer look at the templates you can work with, and provided a high-level technical overview—so I won’t be covering any of that here.

Instead, I’ll be working through an implementation of a custom style transfer Lens—from building the model, to integrating it in Lens Studio, to deploying and testing it within Snapchat.

But if you need or would like to get caught up on SnapML first, I’d encourage you to check out these resources from our team:

Lens Studio 3.0 introduces SnapML for adding custom neural networks directly to Snapchat

Including a conversation with Hart Woolery, CEO of 2020CV and a SnapML creator

heartbeat.fritz.ai

Exploring SnapML: A Technical Overview

Our high-level technical overview of Lens Studio’s new framework for custom machine learning models

heartbeat.fritz.ai

Working with SnapML Templates in Lens Studio: An Overview

Add powerful ML features to you Snap Lenses using SnapML’s easy-to-use templates

heartbeat.fritz.ai


The Style Transfer Template

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Lens Studio Home — Note the Style Transfer option under “Recommended Templates”

Before we jump into building our model and Lens, I first want to give you a sense of what’s included in the Lens Studio’s Style Transfer template.

Essentially, each ML-based Lens Studio template includes:

  • A preconfigured project inside Lens Studio with an ML Component (Snap’s term for a container that holds the ML model file and input/output configurations) implemented. There’s also a sample model included by default.
  • Access to a zip file that includes a Jupyter Notebook file (.ipynb) that you can run inside Google Colab. You’ll want to download this and unzip it (see below). I created a primary project folder on my local machine and nested this folder inside it.
  • Other important project files, depending on the ML task at hand (for Style Transfer, Snap provides a sample style reference image and a content/test image).

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Contents of the Style Transfer zip file.


Training a Custom Style Transfer Model in Google Colab

I was pleasantly surprised at how easy it was to work with the Notebook the Snap team had prepared. Let’s work through the steps below:

Step 1: Upload Style Transfer Notebook

Simply open Google Colab, and in the intro flow where you’re prompted to choose a project/Notebook, drag-and-drop the provided Python Notebook file. Once you’ve done that, Colab should automatically connect to the appropriate runtime—so for the purposes of this project, you won’t need to adjust anything there.

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Drag-and-drop the provided model file here.

Step 2: Upload Style and Reference (Test) Images

Because we’re training our own custom style transfer model, we’ll need to supply our model with the necessary training inputs: a style image and a _content reference _image.

With other templates (i.e. Classification, Segmentation), you’d need to upload full image datasets, but here, given the model architecture and task, you’ll only need to upload two images. These are the ones I chose:

Image for post

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Left: Style Image; Right: Content Reference Image

To upload these, simply drag-and-drop them into the “Files” tab on the left-side panel. Click “Ok” when notified that exiting the runtime will delete these files — we should have a fully trained model once we’re done with this process, so no need to worry about that.

_Important note: _To ensure the model accepts these two input images, they must be named _style_image_ and _test_image_, respectively. They should be in _.png_ format.

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Step 3: Train the Model

Because all the model needs is a single style image to train on and a reference image to test on, we’re ready to train our model once we’ve uploaded these two images.

If you scroll through the Notebook you’ll notice each step in the process is commented out, with explanations for what’s happening at each step. I like this kind of additional detail, as it gives small windows into how the model is trained at each step, along with the requisite code blocks. So if you’re interested, I’d encourage you to sift through it to get a flavor of Snap’s magic sauce.

But if you just want to train your model, click **Runtime **in the top nav bar and choose the first option, **Run All. **This will automatically run all code blocks in succession, all the way from installing the necessary libraries to importing training data (the model leverages the COCO dataset to help train) to training, converting, and downloading the model.

#lens-studio #heartbeat #mobile-machine-learning #snapchat #machine-learning #deep learning

Alice Cook

Alice Cook

1614329473

Fix: G Suite not Working | G Suite Email not Working | Google Business

G Suite is one of the Google products, developed form of Google Apps. It is a single platform to hold cloud computing, collaboration tools, productivity, software, and products. While using it, many a time, it’s not working, and users have a question– How to fix G Suite not working on iPhone? It can be resolved easily by restarting the device, and if unable to do so, you can reach our specialists whenever you want.
For more details: https://contactforhelp.com/blog/how-to-fix-the-g-suite-email-not-working-issue/

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