We are here to offer a lot more than just training models, as common AI companies do. Our purpose is not to develop AGI (artificial general intelligence), which is going to take over the world, but easy-to-use AI solutions, that can revolutionize many areas of both business and daily life. So, let’s dive into the possibilities of flows in this 2021 update of one of our most-viewed articles.

Flows: Visual AI Setup Cannot Get Much Easier

In general, at our platform, you can break your machine learning problem down into smaller, separate parts (recognition, detection, and other machine learning models called tasks) and then easily chain & combine these tasks with Flows to achieve the full complexity and hierarchical classification of a visual AI solution.

A typical simple use case is conditional image processing. For instance, the first recognition task filters out non-valid images, then the next one decides a category of the image and, according to the result, other tasks recognize specific features for a given category.

Flows allow your team to review and change datasets of all complexity levels fast and without any trouble. It doesn’t matter whether your model uses three simple categories (e.g. cats, dogs, and guinea pigs) or works with an enormous and complex hierarchy with exceptions, special conditions, and interdependencies.

It also enables you to review the whole dataset structure, analyze, and, if necessary, change its logic due to modularity. With a few clicks, you can add new labels or models (tasks), change their chaining, change the names of the output fields, etc. Neat? More than that!

Define a Flow With a Few Clicks

Let’s assume we are building a real estate website, and we want to automatically recognize different features that we can see in the photos. Different kinds of apartments and houses have various recognizable features. Here is how we can define this workflow using recognition flows (we trained each model with a custom image recognition service):

The image recognition models are chained in a “main” flow called the branch selector. The branch selector saves the result in the same way as a recognition task node and also chooses an action based on the result of this task. First, we let the top category task recognize the type of estate (Apartment vs. Outdoor house). If it is an apartment, we can see that two subsequent tasks are “Apartment features” and “Room type”.

A flow can also call other flows, so-called nested flows, and delegate part of the work to them. If the image is an outdoor house, we continue processing by another nested flow called “Outdoor house”. In this flow, we can see another branching according to the task that recognizes “House type”. Different tasks are called for individual categories (Bungalow, Cottage, etc.):

Flow Elements We Used

So far, we have used three elements:

• A recognition task, that simply calls a given task and saves the result into an output field with a specified name. No other logic is involved.
• A branch selector, on the other hand, saves the result in the same way as a recognition task node, but then it chooses an action based on the result of this task. 
• Nested flow, another flow of tasks, that the “main” flow (branch selector) called.

Implicitly, there is also a List element present in some branches. We do not need to create it, because as soon as we add two or more elements to a single branch, a list generates in the background. All nodes in a list are normally executed in parallel, but you can also set sequential execution. In this case, the reordering button will appear.

Branch Selector – Advanced Settings

The branch selector is a powerful element. It’s worthwhile to explore what it can do. Let’s go through the most important options. In a single branch, by default, only actions (tag or category) with the highest relevance will be performed, provided the relevance (the probability outputted by the model) is above 50 %. But we can change this in advanced settings. We can specify the threshold value and also enable parallel execution of multiple branches!

You can specify the format of the results. Flat JSON means that results from all branches will be saved on the same level as any previous outcomes. And if there are two same output names in multiple branches, they can be overwritten. The parallel execution guarantees neither order nor results. You can prevent this from happening by selecting nested JSON, which will save the results from each branch under a separate key, based on the branch name (that is the tag/category name).

If some data (output_field) are present in the incoming request, we can skip calling the branch selector processing. You can define this in If Output Field Exists. This way we can save credits and also improve the precision of the system. I will show you how useful this behaviour can be in the next paragraphs. To learn about the advanced options of training, check this article.

An Example: Fashion Detection With Tags

We have just created a flow to tag simple and basic pictures. That is cool. But can we really use it in real-life applications? Probably not. The reason is, in most pictures, there is usually more than one clothing item. So how are we going to automate the tagging of more complex pictures? The answer is simple: we can integrate object detection into flows and combine it with recognition & tagging models!

The flow structure then exactly mirrors the rich product taxonomy. Each image goes through a taxonomy tree in order to get proper tags. This is our “top classifier” – a flow that can tell one of our seven top categories of a fashion product image, which will determine how the image will be further classified. For instance, if it is a “Clothing” product, the image continues to “Clothing tagging” flow.

Similar to categorization or tagging, there are two basic nodes for object detection: the Detection Task for simple execution of a given task and Object Selector, which enables the processing of the detected objects.

Object Selector will call the object detection task. The detected objects will be extracted out of the image and passed further to any of the available nodes. Yes, any of them! Even another Object Selector, if, for example, you need to first detect people and then detect clothes on each person separately.

Object Selector – Advanced Settings

Object Selector behavior can be customized in similar ways as a Branch Selector. In addition to the Probability Threshold, there is also an Area Threshold. By default, all objects are processed. By setting this threshold, the objects that do not take at least a given percentage of an image are simply ignored. This can be changed to a single object by probability or area in Select. As I mentioned, we extract the object before further processing. We can extend it a bit to include some context using Expand Bounding Box by…

A Typical Flows Application: Fashion Tagging

We have been playing with the fashion subject since the inception of Ximilar. It is the most challenging and also the most promising one. We have created all kinds of tools and helpers for the fashion industry, namely Fashion Tagging, specialized Fashion Search, or Annotate. We are proud to have a very precise automatic fashion tagging service with a rich fashion taxonomy.

And, of course, Fashion Tagging is internally powered by Flows. It is a huge project with several dozens of features to recognize, about a hundred recognition tasks, and hundreds of labels all chained into several interconnected flows. For example, this is what our AI says about a simple dress now – and you can try it on your picture in the public demo.

Include Pre-trained Services In Your Flow

The last group of nodes at your disposal are Ximilar services. We are working hard and on an ever-growing number of ready-to-use services which can be called through our API and integrated into your project. It is natural for our users to combine more AI services, and flows make it easier than ever. At this moment, you can call these ready-to-use recognition services:

• Fashion Tagging (demo, details)
• Home Decor Tagging (demo, details)
• Dominant Colors (demo, details)

But more will come in the future, for example, Remove Background.

Increasing Possibilities of Flows

As our app and list of services grow, so do the flows. There are two features we are currently looking forward to. We are already building custom similarity models for our customers. As soon as they are ready, they will be available for combining in flows. And there is one more item very high on our list, which is predicting numeric values. Regression, in machine learning terms. Stay tuned for more exciting news!

Create Your Flow – It’s Free

Before Flows, setting up the AI Vision process was a tedious task for a skilled developer. Now everyone can set up, manage and alter steps on their own. In a comprehensive, visual way. Being able to optimize the process quickly, getting a faster response, losing less time and expenses, and delivering higher quality to customers.

And what’s the best part? Flows are available to the users of Ximilar’s free plan, so you can try them right away. Register or sign up to the Ximilar App and enter Flows service at the Dashboard. If you want to learn the basics first, check out our video tutorials. Then you can connect tasks and labels defined in your own Image Recognition.

Training of machine learning models is free with Ximilar, you are only paying for API calls for recognition. Read more about API calls or API credit packs. We strongly believe you will love Flows as much as we enjoyed bringing them to life. And if you feel like there is a feature missing, or if you prefer a custom-made solution, feel free to contact us!

The post Flows – The Game Changer for Next-Generation AI Systems appeared first on Ximilar: Visual AI for Business.

Every big machine learning project requires the active cooperation of multiple team members – engineers, researchers, annotators, product managers, or owners. For example, supervised deep learning for object detection, as well as segmentation, outperforms unsupervised solutions. However, it requires a lot of data with correct annotations. Annotation of images is one of the most time-consuming parts of every deep learning project. Therefore, picking the right annotator tool is critical. When your team is growing and your projects require higher complexity over time, you may encounter new challenges, such as:

• Adding labels to the taxonomy would require re-checking a lot of your work
• Increasing the performance of your models would require more data
• You will need to monitor the progress of your projects

Building solid annotation software for computer vision is not an easy task. And yes, it requires a lot of failures and taking many wrong turns before finding the best solution. So let’s look at what should be the basic features of an advanced data annotation tool.

What Should an Advanced Image Annotation Tool Do?

Many customers are using our cloud platform Ximilar App in very specific areas, such as Fashion, Healthcare, Security, or Industry 4.0. The environment of a proper AI helper or tool should be complex enough to cover requirements like:

• Features for team collaboration – you need to assign tasks, and then check the quality and consistency of data
• Great user experience for dataset curation – everything should be as simple as possible, but no simpler
• Fast production of high-quality datasets for your machine-learning models
• Work with complex taxonomies & many models chained with Flows
• Fast development and prototyping of new features
• Connection to Rest API with Python SDK & querying annotated data

With these needs in mind, we created our own image annotation tool. We use it in our internal projects and provide it to our customers as well. Our technologies for machine learning accelerate the entire pipeline of building good datasets. Whether you are a freelancer tagging pictures or a team managing product collections in e-commerce, Annotate can help.

Our Visual AI tools enable you to work with your own custom taxonomy of objects, such as fashion apparel or things captured by the camera. You can read the basics on the categories & tags and machine learning model training, watch the tutorials, or check our demo and see for yourself how it works.

The Annotate

Annotate is an advanced image annotation tool, which enables you to annotate images precisely and fast. It works as an end-to-end platform for visual data management. You can query the same images, change labels, create objects, draw bounding boxes and even polygons here.

It is a web-based online annotation tool, that works fully on the cloud. Since it is connected to the same back-end & database as Ximilar App, all changes you do in Annotate, manifest in your workspace in App, and vice versa. You can create labels, tasks & models, or upload images through the App, and use them in Annotate.

Annotate extends the functionalities of the Ximilar App. The App is great for training, creating entities, uploading data, and batch management of images (bulk actions for labelling and filtering). Annotate, on the other hand, was created for the detail-oriented management of images. The default single-zoomed image view brings advantages, such as:

• Identifying separate objects, drawing polygons and adding metadata to a single image
• Suggestions based on AI image recognition help you choose from very complex taxonomies
• The annotators focus on one image at a time to minimize the risk of mistakes

Interested in getting to know Annotate better? Let’s have a look at its basic functions.

Deep Focus on a Single Image

If you enter the Images (left menu), you can open any image in the single image view. To the right of the image, you can see all the items located in it. This is where most of the labelling is done. There is also a toolbar for drawing objects and polygons, labelling images, and inspecting metadata.

In addition, you can zoom in/out and drag the image. This is especially helpful when working with smaller objects or big-resolution images. For example, teams annotating medical microscope samples or satellite pictures can benefit from this robust tool.

Create Multiple Workspaces

Some of you already know this from other SaaS platforms. The idea is to divide your data into several independent storages. Imagine your company is working on multiple projects at the same time and each of them requires you to label your data with an image annotation tool. Your company account can have many workspaces, each for one project.

Within the workspaces, you don’t mix your images, labels, and tasks. For example, one workspace contains only images for fruit recognition projects (apples, oranges, and bananas) and another contains data on animals (cats and dogs).

Your team members can get access to different workspaces. Also, everyone can switch between the workspaces in the App as well as in Annotate (top right, next to the user icon). Did you know, that the workspaces are also accessible via API? Check out our documentation and learn how to connect to API.

Train Precise AI Models with Verification

Building good computer vision models requires a lot of data, high-quality annotations, and a team of people who understand the process of building such a dataset. In short, to create high-quality models, you need to understand your data and have a perfectly annotated dataset. In the words of the Director of AI at Tesla, Andrej Karpathy:

Annotate helps you build high-quality AI training datasets by verification. Every image can be verified by different users in the workspace. You can increase the precision by training your models only on verified images.

Verifying your data is a necessary requirement for the creation of good deep-learning models. To verify the image, simply click the button verify or verify and next (if you are working on a job). You will be able to see who verified any particular image and when.

Create and Track Image Annotating Jobs

When you need to process the newly uploaded images, you can assign them to a Job and a team of people can process them one by one in a job queue. You can also set up exactly how many times each image should be seen by the people processing this queue.

Moreover, you can specify, which photo recognition model or flow of models should be displayed when doing the job. For example, here is the view of the jobs that we are using in one of our tagging services.

When working on a job, every time an annotator hits the Verify & Next button, it will redirect them to a new image within a job. You can track the progress of each job in the Jobs. Once the image annotation job is complete, the progress bar turns green, and you can proceed to the next steps: retraining the models, uploading new images, or creating another job.

Draw Objects and Polygons

Sometimes, recognizing the most probable category or tags for an image is not enough. That is why Annotate provides a possibility to identify the location of specific things by drawing objects and polygons. The great thing is that you are not paying any credits for drawing objects or labelling. This makes Annotate one of the most cost-effective online apps for image annotation.

What exactly do you pay for, when annotating data? The only API credits are counted for data uploads, with volume-based discounts. This makes Annotate an affordable, yet powerful tool for data annotation. If you want to know more, read our newest Article on API Credit Packs, check our Pricing Plans or Documentation.

Annotate With Complex Taxonomies Elegantly

The greatest advantage of Annotate is working with very complex taxonomies and attribute hierarchies. That is why it is usually used by companies in E-commerce, Fashion, Real Estate, Healthcare, and other areas with rich databases. For example, our Fashion tagging service contains more than 600 labels that belong to more than 100 custom image recognition models. The taxonomy tree for some of the biotech projects can be even broader.

Navigating through the taxonomy of labels is very elegant in Annotate – via Flows. Once your Flow is defined (our team can help you with it), you simply add labels to the images. The branches expand automatically when you add labels. In other words, you always see only essential labels for your images.

For example, in this image is a fashion object “Clothing”, to which we need to assign more labels. Adding the Clothing/Dresses label will expand the tags that are in the Length Dresses and Style Dresses tasks. If you select the label Elegant from Style Dresses, only features & attributes you need will be suggested for annotation.

Automate Repetitive Tasks With AI

Annotate was initially designed to speed up the work when building computer vision solutions. When annotating data, manual drawing & clicking is a time-consuming process. That is why we created the AI helper tools to automate the entire annotating process in just a few clicks. Here are a few things that you can do to speed up the entire annotation pipeline:

• Use the API to upload your previously annotated data to train or re-train your machine learning models and use them to annotate or label more data via API
• Create bounding boxes and polygons for object detection & instance object segmentation with one click
• Create jobs, share the data, and distribute the tasks to your team members

Image Annotation Tool for Advanced Visual AI Training

As the main focus of Ximilar is AI for sorting, comparing, and searching multimedia, we integrate the annotation of images into the building of AI search models. This is something that we miss in all other data annotation applications. For the building of such models, you need to group multiple items (images or objects, typically product pictures) into the Similarity Groups. Annotate helps us create datasets for building strong image similarity search models.

Annotate is Always Growing

Annotate was originally developed as our internal image annotation software, and we have already delivered a lot of successful solutions to our clients with it. It is a unique product that any team can benefit from and improve the computer vision models unbelievably fast. 

We plan to introduce more data formats like videos, satellite imagery (sentinel maps), 3D models, and more in the future to level up the Visual AI in fields such as visual quality control or AI-assisted healthcare. We are also constantly working on adding new features and improving the overall experience of Ximilar services.

Annotate is available for all users with Business & Professional pricing plans. Would you like to discuss your custom solution or ask anything? Let’s talk! Or read how the cooperation with us works first.

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