Searching is one of the most popular activity users conduct on web applications, which yields any kind of content, such as products, images, or news.
In the case of applications handling images, the search procedure typically involves matching only existing metadata, e.g., name, date, and size. However, users rarely know this information due to a large amount of data nowadays available. In such a circumstance, machine learning can be helpful.
We propose a method for integrating web applications (Angular) with machine learning (TensorFlowJs), which facilitates user searching in the following ways. First, it enriches existing image metadata with machine learning by adding further information provided by object detector models. Second, it enables users to search any items by image content rather than existing metadata. Specifically, this workshop will show you how to a) create an angular app utilizing angular cli, b) save permanently images on google storage, c) make use of machine learning models for object recognition and detection, d) save predicted metadata on Google fire cloud, e) finally, search any items by image content.
This document provides an overview of theming Moodle. It discusses:
- Building themes in a sustainable way using parent themes
- Making themes accessible with high-contrast options
- The anatomy of a Moodle theme including configuration files, stylesheets, images and layout templates
- Overriding Moodle's default images and icons from within a theme
- Tools for troubleshooting theme issues like browser inspectors
- Common issues like caching that can prevent theme changes from appearing
Si è tornato a parlare molto di Machine Learning negli ultimi anni. Grazie anche al fatto che è possibile oggi processare enormi moli di dati in tempi (relativamente) veloci questa parte dell'informatica sta vivendo una seconda giovinezza.
In questa sessione vedremo cos'è il machine learning, quali sono le diverse casistiche tecniche e funzionali in cui può essere usato ed inizieremo a "giocare" con i dati per vedere fin dove possiamo spingerci, usando strumenti On-Premise e quindi spostandoci poi sull'offerta Azure Machine Learning dove, una volta fatta propria la teoria, si possono realizzare soluzioni estremamente complesse in modo molto visuale, oppure integrandosi con R ed IPython e sfruttare la scalabilità di Azure per avere performance ottimali. Il tutto senza dimenticare che gli algoritmi così ottenuti possono essere facilmente integrati nelle nostre applicazioni semplicemente invocando un web service.
Building real time image classifiers for mobile apps with azure custom visionLuis Beltran
Azure Custom Vision allows you to create powerful image classifiers in minutes to without having to be an AI expert. You feed the service with images -so the service adapts to your own needs-, tag them and train a model that can be published to an endpoint URL for further requests. You can also use the Custom Vision SDK to automatize the process.
Furthermore, this model can also be exported for offline, real-time classification experiences. For instance, you can embed the classifier into a mobile application, or a website.
In this session, the Custom Vision service will be described. An image classifier will be created by using the .portal. The output model will be exported to both Tensorflow and CoreML to integrate it into an Android and iOS mobile applications, respectively.
Create an image classifier with azure custom vision net sdkLuis Beltran
Azure Custom Vision allows you to build an image classifier that adjusts to your needs without needing an AI/ML background.
In this session we will learn about Custom Vision and how you can train an image classifier model using its .NET SDK. A published model can then be exported to a variety of formats, such as TensorFlow and CoreML, which can perfectly be integrated into Android & iOS mobile apps with object recognition capabilities.
In this session, the Custom Vision service will be explored and demonstrated with: a) a trained model using the COIL-100 image dataset and the Custom Vision .NET SDK. b) A mobile image classifier app which makes use of the model in a) in both online and offline scenarios
Azure ML: from basic to integration with custom applicationsDavide Mauri
In this session, Davide will explore Azure ML from the inside out. After a gentle approach on Machine Learning, we’ll see the Microsoft offering in this field and all the feature it offers, creating a simple yet 100% complete Machine Learning solution.
We’ll start from something simple and then we’ll also move to some more complex topics, such as the integration with R and Python, IPython Notebook until the Web Service publishing and usage, so that we can integrate the created ML solution with batch process or even use it in real time with LOB application.
All of this sound cool to you, yeah? Well it is, since with ML you can really give that “something more” to your customers or employees that will help you to make the difference. Guaranteed at 98.75%!
Accelerating Data Science and Machine Learning Workflow with Azure Machine Le...Aditya Bhattacharya
Accelerating Data Science and Machine Learning Workflow with Microsoft Azure Machine Learning
Microsoft User Group Hyderabad AIML Day 2020
https://aditya-bhattacharya.net/
https://www.eventbrite.com/e/microsoft-user-group-hyderabad-aiml-day-2020-tickets-123940376001
MUGH
Develop faster on Magento 2 using code generation toolsJuan Alonso
This document discusses using code generation tools to develop faster in Magento 2. It summarizes the speaker's experience with repetitive code and issues with different file formats in Magento 2 projects. The speaker found solutions in PHPStorm templates and code generation tools that can automatically generate files and folders to quickly set up modules. Existing tools are presented like Pestle, N98-magerun2, and Mage2Gen. The speaker's own generator called Magento2 Code Generator is demonstrated, which aims to easily extend and contribute new templates. Tips for PHPStorm external tools are also provided.
Drupal theming - a practical approach (European Drupal Days 2015)Eugenio Minardi
This document discusses best practices for Drupal theming. It covers topics like directory layout, basic theme elements, render arrays, hooks, overriding templates and functions, common mistakes, and more. Tools like VirtualBox and Vagrant are recommended for local development. Templates should be used for display only and advanced logic should be moved to preprocess functions. Security risks can arise from directly editing core/module files or using global variables.
This document provides an overview of theming Moodle. It discusses:
- Building themes in a sustainable way using parent themes
- Making themes accessible with high-contrast options
- The anatomy of a Moodle theme including configuration files, stylesheets, images and layout templates
- Overriding Moodle's default images and icons from within a theme
- Tools for troubleshooting theme issues like browser inspectors
- Common issues like caching that can prevent theme changes from appearing
Si è tornato a parlare molto di Machine Learning negli ultimi anni. Grazie anche al fatto che è possibile oggi processare enormi moli di dati in tempi (relativamente) veloci questa parte dell'informatica sta vivendo una seconda giovinezza.
In questa sessione vedremo cos'è il machine learning, quali sono le diverse casistiche tecniche e funzionali in cui può essere usato ed inizieremo a "giocare" con i dati per vedere fin dove possiamo spingerci, usando strumenti On-Premise e quindi spostandoci poi sull'offerta Azure Machine Learning dove, una volta fatta propria la teoria, si possono realizzare soluzioni estremamente complesse in modo molto visuale, oppure integrandosi con R ed IPython e sfruttare la scalabilità di Azure per avere performance ottimali. Il tutto senza dimenticare che gli algoritmi così ottenuti possono essere facilmente integrati nelle nostre applicazioni semplicemente invocando un web service.
Building real time image classifiers for mobile apps with azure custom visionLuis Beltran
Azure Custom Vision allows you to create powerful image classifiers in minutes to without having to be an AI expert. You feed the service with images -so the service adapts to your own needs-, tag them and train a model that can be published to an endpoint URL for further requests. You can also use the Custom Vision SDK to automatize the process.
Furthermore, this model can also be exported for offline, real-time classification experiences. For instance, you can embed the classifier into a mobile application, or a website.
In this session, the Custom Vision service will be described. An image classifier will be created by using the .portal. The output model will be exported to both Tensorflow and CoreML to integrate it into an Android and iOS mobile applications, respectively.
Create an image classifier with azure custom vision net sdkLuis Beltran
Azure Custom Vision allows you to build an image classifier that adjusts to your needs without needing an AI/ML background.
In this session we will learn about Custom Vision and how you can train an image classifier model using its .NET SDK. A published model can then be exported to a variety of formats, such as TensorFlow and CoreML, which can perfectly be integrated into Android & iOS mobile apps with object recognition capabilities.
In this session, the Custom Vision service will be explored and demonstrated with: a) a trained model using the COIL-100 image dataset and the Custom Vision .NET SDK. b) A mobile image classifier app which makes use of the model in a) in both online and offline scenarios
Azure ML: from basic to integration with custom applicationsDavide Mauri
In this session, Davide will explore Azure ML from the inside out. After a gentle approach on Machine Learning, we’ll see the Microsoft offering in this field and all the feature it offers, creating a simple yet 100% complete Machine Learning solution.
We’ll start from something simple and then we’ll also move to some more complex topics, such as the integration with R and Python, IPython Notebook until the Web Service publishing and usage, so that we can integrate the created ML solution with batch process or even use it in real time with LOB application.
All of this sound cool to you, yeah? Well it is, since with ML you can really give that “something more” to your customers or employees that will help you to make the difference. Guaranteed at 98.75%!
Accelerating Data Science and Machine Learning Workflow with Azure Machine Le...Aditya Bhattacharya
Accelerating Data Science and Machine Learning Workflow with Microsoft Azure Machine Learning
Microsoft User Group Hyderabad AIML Day 2020
https://aditya-bhattacharya.net/
https://www.eventbrite.com/e/microsoft-user-group-hyderabad-aiml-day-2020-tickets-123940376001
MUGH
Develop faster on Magento 2 using code generation toolsJuan Alonso
This document discusses using code generation tools to develop faster in Magento 2. It summarizes the speaker's experience with repetitive code and issues with different file formats in Magento 2 projects. The speaker found solutions in PHPStorm templates and code generation tools that can automatically generate files and folders to quickly set up modules. Existing tools are presented like Pestle, N98-magerun2, and Mage2Gen. The speaker's own generator called Magento2 Code Generator is demonstrated, which aims to easily extend and contribute new templates. Tips for PHPStorm external tools are also provided.
Drupal theming - a practical approach (European Drupal Days 2015)Eugenio Minardi
This document discusses best practices for Drupal theming. It covers topics like directory layout, basic theme elements, render arrays, hooks, overriding templates and functions, common mistakes, and more. Tools like VirtualBox and Vagrant are recommended for local development. Templates should be used for display only and advanced logic should be moved to preprocess functions. Security risks can arise from directly editing core/module files or using global variables.
Azure Resource Manager templates: Improve deployment time and reusabilityStephane Lapointe
Azure Resource Manager is the future of Azure and his templating features are a big improvement and simplification of how you provision resources on Azure. See how you can create ARM template in Visual Studio to create complex, multiple resources templates and how they can be combined and reused. Learn the different template functions available and how they can help you build more advanced template.
DNN-Connect 2019: Build a Module in MinutesWill Strohl
In this DNN-Connect 2019 session, we discussed a new architecture to use to build your extensions. This allows you to have and manage your various DNN extensions all in one solution.
Learn to visually build, train, and deploy custom machine learning models without machine learning expertise. We will be using Model Builder and Auto ML for the Demo.
This document provides an overview of deep learning concepts using PyTorch. It begins with definitions of machine learning, deep learning, and neural networks. It then discusses what PyTorch is and why it is commonly used, as well as key PyTorch concepts like tensors. The document outlines what will be covered in the course, including PyTorch fundamentals, preprocessing data, building models, training models, evaluation, and making predictions. It encourages an experimental approach to learning and offers resources for further exploration.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Azure Resource Manager templates: Improve deployment time and reusabilityStephane Lapointe
Azure Resource Manager is the future of Azure and his templating features are a big improvement and simplification of how you provision resources on Azure. See how you can create ARM template in Visual Studio to create complex, multiple resources templates and how they can be combined and reused. Learn the different template functions available and how they can help you build more advanced template.
DNN-Connect 2019: Build a Module in MinutesWill Strohl
In this DNN-Connect 2019 session, we discussed a new architecture to use to build your extensions. This allows you to have and manage your various DNN extensions all in one solution.
Learn to visually build, train, and deploy custom machine learning models without machine learning expertise. We will be using Model Builder and Auto ML for the Demo.
This document provides an overview of deep learning concepts using PyTorch. It begins with definitions of machine learning, deep learning, and neural networks. It then discusses what PyTorch is and why it is commonly used, as well as key PyTorch concepts like tensors. The document outlines what will be covered in the course, including PyTorch fundamentals, preprocessing data, building models, training models, evaluation, and making predictions. It encourages an experimental approach to learning and offers resources for further exploration.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
8. Cloud store Machine Learning Metadata Search by content
&@mauriziovitale_
Store Image Add metadata Save metadata Search image
Batch of images
InputOutput
Google Firestorage TensorflowJs Model Google Firestore Angular
Good afternoon, today I will start by giving you a little introduction about the issue of searching for images by content. Then I will show you how TensorflowJs can help us to solve it. And finally, I will show you a demo of how the final application works and how to code it.
Searching is the most common activity that users do on the web.
And these are the current hot trend keywords coming from Google Trends right now.
As you can see, people are searching for football teams, celebrities and recent events. When it comes to searching for images, it is the content within the images that people are interested in.
For example, which objects do you recognize in this image?
We can see a chair, a dinner table, two bottles of sauce and a sandwich. Each of these items can be represented in JSON format. This is how we store the info of an object, the position, the name and a score. The score is a value between ZERO and ONE which indicates how likely the object represents one particular class. For example, in this case, the model is 0.76% sure that this object is a sandwich.
If we take one step back, we can notice that thanks to our brain and our ability to recognize objects, we are able to add this metadata manually. This is a really difficult (and time consuming) task and you will need hundreds or thousands of lines of code to make a computer recognize it with the traditional approach.
So what if there was a different way to teach a computer how to recognise what it sees. What if you could have a computer learn in the same way that a human does. This is the core of machine learning and this is where Tensorflowjs comes in. Thanks to a pre-trained model, coco-ssd, we will be able to detect all this metadata automatically.
Once we have this info, it's easy to fetch all images that contain, for example, a sandwich.
Searching is the most common activity that users do on the web.
And these are the current hot trend keywords coming from Google Trends right now.
As you can see, people are searching for football teams, celebrities and recent events. When it comes to searching for images, it is the content within the images that people are interested in.
For example, which objects do you recognize in this image?
We can see a chair, a dinner table, two bottles of sauce and a sandwich. Each of these items can be represented in JSON format. This is how we store the info of an object, the position, the name and a score. The score is a value between ZERO and ONE which indicates how likely the object represents one particular class. For example, in this case, the model is 0.76% sure that this object is a sandwich.
If we take one step back, we can notice that thanks to our brain and our ability to recognize objects, we are able to add this metadata manually. This is a really difficult (and time consuming) task and you will need hundreds or thousands of lines of code to make a computer recognize it with the traditional approach.
So what if there was a different way to teach a computer how to recognise what it sees. What if you could have a computer learn in the same way that a human does. This is the core of machine learning and this is where Tensorflowjs comes in. Thanks to a pre-trained model, coco-ssd, we will be able to detect all this metadata automatically.
Once we have this info, it's easy to fetch all images that contain, for example, a sandwich.
I have already mentioned one TensorFlowJs model but there are actually other models available out there too.
Let me show you a couple of examples. We have coco-ssd which is able to detect and recognize 90 different classes of objects. Mobilenet, which is optimized for the mobile. Another one is body pix which is able to recognize the pixels of your body so that you can remove the background or do segmentation. The last one that I'd like to show is posenet which shows the joints of the body.
As I said, these models are already trained. You don't need to know how ML works or what a loss function or the bias is. You just need to load the model and use it.
Of course, with TensorflowJs you can create and train your own model. If you are interested in such a thing, I can recommend some videos by these guys (Lauren and Hussain).
Now that you have an idea of what we can do with TensorflowJs, let's focus on the problem we are trying to solve in this talk.
Imagine having a repository with thousands of images with useless names. As a user, you know the content you are looking for but you don't know the file name. So you write 'cat' in the search and you see that it doesn't give you any results. This is because there is no metadata that contains such info. To solve the problem what we can do is add this info in the description field and if we try again, we will see that the file is now there.
Because we don't like to do manual stuff, we will build our own application, which uses ML to automate this process.
These are the steps and libraries that our angular app will use. Just to give you a quick overview, we have:
- Google Firestorage, which is used to store files in the cloud.
- The Tensorflowjs model which detects the objects.
- Firestore which is a Real time database where we store our data...
...and Angular, which is used to create our app.
Let's see the flow in action:
We have the images that we want to upload, so what do we do? We select one and with Firestorage, we upload the image to the cloud. This means that the next time the user visits the website, the image will still be there.
Then we will pass the image to the model. We can imagine that a model is like a function. It needs an input and it will return an output. In this case the input is the image and the output the metadata.
After that, we will save this metadata in Firestore and finally, we will be able to search our images by content.
But let's see how it works in practice...
This is what our final application looks like.
On the left hand side, we have our application and on the right hand side, we have the firestore/storage console.
Here, I am uploading a couple of images and
Every time we upload an image, we store the image in the cloud and we create an instance on the firestore that contains the basic info such as name, location of the file. For now, it doesn't contain the detected objects.
Then, when we click on the recognize button we pass the image to the tensorflow model that will detect the objects and will return the classes that can be displayed. With this new info, we can enrich and update the image metadata on firestore.
Once we have all the metadata classes for each image, we can easily search the images based on the content.
As a final step, we can delete the image from both storage and firestore
Now let's go ahead and build this thing.
We start with a brand new angular app. we use the angular cli new to generate a new project.
You can select your preference and in a couple of seconds you have a basic angular app.
Then we install angularfire2 dependency that allows us to use firestore and firestorage
And finally we install TensorFlow model coco-ssd.
The first step is to import all the modules in our recognition module and initialize our angular fire app. You can get the firebase settings from the google console.
Inside the RecognitionModule we need to initialise our angular fire app with the project config that you can pick from the firebase console.
Once we have imported all the modules, we can generate a component that will contain our upload widget.
Starting from the HTML we have a simple input button that is listening to the changeEvent. When this event is fired, the method upload will be triggered. We are only allowing the png and jpg images files to be uploaded.
To improve the UI a little bit, we are hiding the default input file element and we use angular material button which has an icon and then choose a file sentence. Here we are binding the two elements and the result is that every time we click on the material button, the upload method will be called.On the component side we have the implementation of the upload method which has as input the list of files.
We can get the first file from the list by calling target dot files 0. To be able to upload a file in firestorage cloud bucket we need a unique id that represents the location of the file in the bucket. So we generate a random id with math random.
At this point, we have every thing we need to save the file in the cloud.
We inject the angular fire storage in the constructor and we use it to make a reference to the actual location in the bucket than calling the put will start the actual upload of the file. Let's see what the app looks like at this point.
This is the same code that I was showing you before. The only difference is that we are now taking a reference of the AngularFireUploadTask which does most of the magic for the file upload itself. While the file is uploading, it provides a couple of observables that we can listen to in order to monitor the progress.
Here we use the snapshot changes which is an observable that emits an object every few hundred milliseconds that contains the data about the file upload. Because our goal is to get the DownloadUrl we need to be sure that the upload is completed.
We can use the finalize operator of rxJs that runs after the observable is completed. Because the getDonwnloadUrl returns a promise, we can create an async function and resolve the promise with the await.
At this point we have all the info so it can be stored in firestore the real time database.
We create an object that contains the info that we want to save on the database, specifically the name, class (that for now is empty but will contain the detected objects within the image) and the location of the file.
We inject an angularfirestore and we make a reference to the image collection and then we add the document to the database.
The next step is to render all the images saved on the database on the page.
So we generate a new component still using the cli., I am skipping this step here
Then we inject the AngularFirestore into the constructor and we get a reference of the same collection as before.
Here we could subscribe to that collection to get all the items but the problem is that in this way we won't have the id of the single item.
So what we do is we subscribe to the snapshotChanges observable and basically we merge the data of the document and the id of the document.
On the HTML side, I am using a material card just to have a nice UI. And with the directive we are subscribing to the collection with async pipe and with NgFor we display the info like the name of the image and the content of the image. Then we use the mat chip to show the different classes detected and finally we have a recognize button. Which calls the recognize method using the item and the reference of the image as input.
if you have a specific problem with TesnsorflowJs you to create your own neural network.There is no network latency because we are not calling any external API such as Amazon ML or AzureBecause we are not calling any API your content is safe because it remains on the device This means that you can do recognition even without a connection