Our AI-powered Product Description revolutionizes the way you manage your fashion apparel catalogs by fully automating the creation of product titles and descriptions. Instead of spending hours manually tagging and writing descriptions, our AI-driven generator swiftly produces optimized texts, saving you valuable time and effort.

With the ability to customize style, tonality, format, length, and preferred product tags, you can ensure that each description aligns perfectly with your brand’s voice and SEO needs. This service is designed to streamline your workflow, providing accurate, engaging, and search-friendly descriptions for your entire fashion inventory.

Enhanced Taxonomy for Accessories Product Tagging

We’ve upgraded our taxonomy for accessories tagging. For sunglasses and glasses, you can now get tags for frame types (Frameless, Fully Framed, Half-Framed), materials (Combined, Metal, Plastic & Acetate), and shapes (Aviator, Cat-eye, Geometric, Oval, Rectangle, Vizor/Sport, Wayfarer, Round, Square). Try how it works on your images in our public demo.

Automate Detection & Tagging of Home Decor Images With AI

Our new Home Decor Tagging service streamlines the process of categorizing and managing your home decor product images. It uses advanced recognition technology to automatically assign categories, sub-categories, and tags to each image, making your product catalog more organized. You can customize the tags and choose translations to fit your needs.

The service also offers flexibility with custom profiles, allowing you to rename tags or add new ones based on your requirements. For pricing details and to see the service in action, check our API documentation or contact our support team for help with custom tagging and translations.

Visual Search for Home Decor: Find Products With Real-Life Photos

With our new Home Decor Search service, customers can use real-life photos to find visually similar items from your furniture and home decor catalogue.

Our tool integrates four key functionalities: home decor detection, product tagging, colour extraction, and visual search. It allows users to upload a photo, which the system analyzes to detect home decor items and match them with similar products from your inventory.

To use Home Decor Search, you first sync your database with Ximilar’s cloud collection. This involves processing product images to detect and tag items, and discarding the images immediately after. Once your data is synced, you can perform visual searches by submitting photos and retrieving similar products based on visual and tag similarity.

The API allows for customized searches, such as specifying exact objects of interest or integrating custom profiles to modify tag outputs. For a streamlined experience, Ximilar offers options for automatic synchronization and data mapping, ensuring your product catalog remains up-to-date and accurate.

The post New Solutions & Innovations in Fashion and Home Decor AI appeared first on Ximilar: Visual AI for Business.

AI’s Impact on Fashion: Turning the Industry on Its Head

The latest buzz in the fashion e-commerce realm revolves around visual AI. From AI-powered fashion design to AI-generated fashion models, and all the new AI tools, which rapidly change our shopping experience by quietly fueling the product discovery engines in the background, often unnoticed.

Key AI-Powered Technologies in Fashion E-Commerce

So what are the main AI technologies shaking up fashion e-commerce lately? And why is it important to keep up with them?

Recognition, Detection & Data Enrichment in Fashion

In the world of fashion e-commerce, time is money. Machine learning techniques now allow fashion e-shops to upload large unstructured collections of images and extract all the necessary information from them within milliseconds. The results of fashion image recognition (tags/keywords) serve various purposes like product sorting, filtering, searching, and also text generation.

These tools are indispensable for today’s fashion shops and marketplaces, particularly those with extensive stock inventories and large volumes of data. In the past few years, automated fashion tagging has made time-consuming manual product tagging practically obsolete.

Generative AI Systems for Fashion

The fashion world has embraced generative artificial intelligence almost immediately. Utilizing advanced AI algorithms and deep learning, AI can analyze images to extract visual attributes such as styles, colours, and textures, which are then used to generate visually stunning designs and written content. This offers endless possibilities for creating personalized shopping experiences for consumers.

Our AI also enables you to automate the writing of all product titles and product descriptions via API, directly utilizing the product attributes extracted with deep tagging and letting you select the tone, length, and other rules to get SEO-friendly texts quickly. We’ll delve deeper into this later on.

Fashion Discovery Engines and Recommendation Systems

Fashion search engines and personalized recommendations are game-changers in online shopping. They are powered by our speciality: visual search. This technology analyzes images in depth to capture their essence and search vast product catalogs for identical or similar products. Three of its endless uses are indispensable for fashion e-commerce: similar items recommendations, reverse image search and image matching.

Visual search enables shoppers to effortlessly explore new styles, find matching pieces, and stay updated on trends. It allows you to have your own visual search engine, that rapidly scans image databases with millions of images to provide relevant and accurate search results within milliseconds. This not only saves you time but also ensures that every purchase feels personalized.

Shopping Assistants in Fashion E-Commerce and Retail

The AI-driven assistants guide shoppers towards personalized outfit choices suited for any occasion. Augmented Reality (AR) technology allows shoppers to virtually try on garments before making a purchase, ensuring their satisfaction with every selection. Personalized styling advice and virtual try-ons powered by artificial intelligence are among the hottest trends developed for fashion retailers and fashion apps right now.

Both fashion tags for occasions extracted with our automated product tagging, as well as similar item recommendations, are valuable in systems that assist customers in dressing appropriately for specific events.

My Fashion Website Needs AI Automation, What Should I Do?

Consider the Needs of Your Shoppers

To provide the best customer experience possible, always take into account your shoppers’ demographics, geographical location, language preferences, and individual styles.

However, predicting style is not an easy task. But by utilizing AI, you can analyze various factors such as user preferences, personal style, favoured fashion brands, liked items, items in their shopping baskets, and past purchases. Think about how to help them discover items aligned with their preferences and receive only relevant suggestions that inspire rather than overwhelm them.

There are endless ways to improve a fashion e-shop. Always keep in mind not to overwhelm the visitors, and streamline your offer to the most relevant items.

While certain customer preferences can be manually set up by users when logging into an app or visiting an e-commerce site, such as preferred sizes, materials, or price range, others can be predicted. For example, design preferences can be inferred based on similarities with items visitors have browsed, liked, saved, or purchased.

Three Simple Steps to Elevate Your Fashion Website With AI

Whether you run a fashion or accessories e-shop, or a vintage fashion marketplace, using these essential AI-driven features could boost your traffic, improve customer engagement, and get you ahead of the competition.

Automate Product Tagging & Text Generation

The image tagging process is fueled by specialised object detection and image recognition models, ensuring consistent and accurate tagging, without the need for any additional information. Our AI can analyze product images, identify all fashion items, and then categorize and assign relevant tags to each item individually.

In essence, you input an unstructured collection of fashion images and receive structured metadata, which you can immediately use for searching, sorting, filtering, and product discovery on your fashion website.

The keywords extracted by AI can serve right away to generate captivating product titles and descriptions using a language model. With Ximilar, you can pre-set the tone and length, and even set basic rules for AI-generated texts tailored for your website. This automates the entire product listing process on your website through a single API integration.

Streamline and Automate Collection Management With AI

Visual AI is great for inventory management and product gallery assembling. It can recognize and match products irrespective of lighting, format, or resolution. This enables consistent image selection for product listings and galleries.

You can synchronise your entire fashion apparel inventory via API to ensure continual processing by up-to-date visual AI. You can either set the frequency of synchronization (e.g., the first day of each month) or schedule the synchronization run every time you add a new addition to the collection.

For example, you can showcase all clothing items on models in product listings or display all accessories as standalone photos in the shopping cart. Additionally, you can automate tasks like removing duplicates and sorting user-generated visual content, saving a lot of valuable time. Moreover, AI can be used to quickly spot inappropriate and harmful content.

Provide Relevant Suggestions & Reverse Image Search

During your collection synchronisation, visual search processes each image and each product in it individually. It precisely analyzes various visual features, such as colours, patterns, edges and other structures. Apart from the inventory curation, this will enable you to:

1. Have your custom fashion recommendation system. You can provide relevant suggestions from your inventory anywhere across the customer journey from the start page to the kart.
2. Improve your website or app with a reverse image search tool. Your visitors can search with smartphone photos, product images, pictures from Pinterest, Instagram, screenshots, or even video content.

Since fashion detection, image tagging and visual search are the holy trinity of fashion discovery systems, we’ve integrated them into a single service called Fashion Search. Check out my article Everything You Need to Know About Fashion Search to learn more.

Visual search can match images, independent of their origin (e.g., professional images vs. user-generated content), quality and format. We can customize it to fit your collection, even for vintage pieces, or niche fashion brands. For a firsthand experience of how basic fashion visual search operates, check out our free demo.

How Does the Automated Fashion Tagging Work?

Let’s take a closer look at the basic AI-driven tool for the fashion industry: automated fashion tagging. Our product tagging is powered by a complex hierarchy of computer vision models, that work together to detect and recognize all fashion products in an image. Then, each product gets one category (e.g., Clothing), one or more subcategories (e.g., Evening dresses or Cocktail dresses), and a varied set of product tags.

To name a few, fashion tags describe the garment’s type, cut, fit, colours, material, or patterns. For shoes, there are features such as heels, toes, materials, and soles. Other categories are for instance jewellery, watches, and accessories.

The fashion taxonomy encompasses hundreds of product tags for all typical categories of fashion apparel and accessories. Nevertheless, we continually update the system to keep up with emerging trends in the fashion industry. Custom product tags, personal additions, taxonomy mapping, and languages other than the default English are also welcomed and supported. The service is available online – via API.

How Do I Use the Automated Fashion Tagging API?

You can seamlessly integrate automated fashion tagging into basically any website, store, system, or application via REST API. I’d suggest taking these steps first:

First, log into Ximilar App – After you register into Ximilar App, you will get the unique API authentication token that will serve for your private connection. The App has many useful functions, which are summarised here. In the past, I wrote this short overview that could be helpful when navigating the App for the first time.

If you’d like to try creating and training your own additional machine learning models without coding, you can also use Ximilar App to approach our computer vision platform.

Secondly, select your plan – Use the API credit consumption calculator to estimate your credit consumption and optimise your monthly supply. This ensures your credit consumption aligns with the actual traffic on your website or app, maximizing efficiency.

And finally, connect to API – The connection process is described step by step in our API documentation. For a quick start, I suggest checking out First Steps, Authentication & Image Data. Automated Fashion Tagging has dedicated documentation as well. However, don’t hesitate to reach out anytime for guidance.

Do You Need Help With the Setup?

Our computer vision specialists are ready to assist you with even the most challenging tasks. We also welcome all suggestions and custom inquiries to ensure our solutions meet your unique needs. And if you require a custom solution, our team of developers is happy to help.

We also offer personalized demos on your data before the deployment, and can even provide dedicated server options or set up offline solutions. Reach out to us via live chat for immediate assistance and our team will guide you through the entire process. Alternatively, you can contact us via our contact page, and we will get back to you promptly.

The post How Fashion Tagging Works and Changes E-Commerce? appeared first on Ximilar: Visual AI for Business.

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.

The post Image Annotation Tool for Teams appeared first on Ximilar: Visual AI for Business.

Customer Experience is Important

It all starts and ends with the customer experience. According to PwC, a good experience leaves people feeling heard and appreciated. And a disappointing experience, on the other hand, drives them away almost instantaneously. When the respondents were asked what they value the most, 80 % chose efficiency, convenience and friendly service, and 75 % chose up-to-date technology.

80 % of American consumers say that speed, convenience, knowledgeable help and friendly service are the most important elements of a positive customer experience. Prioritize technologies that provide these benefits rather than adopting new technologies for the sake of being cutting edge.

PwC, Experience is Everything

The KPMG 2018 research findings were similar: customer experience is more influential than ever. More importantly, AI gains more clout by delivering personalized, customized, and localized experiences to customers. AI, such as machine learning algorithms, intelligent search, or chatbots, take part in the entire product and service cycle nowadays. In addition, Accenture recently wrote that AI, neural networks, and image search are the leading technologies behind the apps taking over the market. And that they are important trends to watch in the following years.

Fashion E-Commerce Trends in 2021

Big trends in current fashion e-commerce are sustainability, slow fashion, second-hand apparel, and resale. For example, the second-hand clothes marketplace Vinted is growing exponentially. According to THREDUP, there are more second-hand shoppers than ever before, and the resale is going to be bigger than fast fashion by 2029.

There were major changes to consumer behaviour as well during the coronavirus pandemic. Glami’s 2021 Fashion Research showed that an astonishing half of the shops moved to the online world, and the number of online consumers is still growing. They are looking for visual inspiration, want shopping to be intuitive and fast, and expect personalized experiences more than ever.

What is Fashion Search?

The Fashion Search is an advanced Visual AI service, custom-built for the fashion industry. It enables you to create your own product discovery system, work smarter with data, and build a better customer experience. Fashion Search brings together the services and features most requested by our fashion e-commerce customers:

1. Object Detection & Fashion Tagging
   The fashion apparel in your images is automatically detected and tagged. Our Fashion Tagging works with a hundred recognition models, hundreds of labels, and dozens of features, all chained into interconnected flows, enabling you to add content 24/7. We refine the quality and add new fashion attributes (features, categories & tags) to this service constantly. However, custom tags are welcomed as well. Read more about Fashion Tagging here.
   
   
2. Product Similarity & Search by Photo
   This Visual Similarity combo enables you to provide a personalized customer experience. The Product Similarity finds and suggests similar products to the item your customer is viewing. As a result, the click rate increases up to 380 %. Search by Photo accepts the pictures your visitors uploaded and automatically recommends similar items from your collection. For example, it can analyze fashion influencer photos and help your customers find trending items on your site. Read more about Visual Search in Fashion or How to Build Superfast Image Similarity for your Website.
   
   
3. Synchronizing Product Data on Cloud
   Our customers’ image databases are synchronized to their private collections on the Ximilar cloud. When you upload new products or images on your website, our AI automatically recognizes the fashion attributes and provides tags. There are two major benefits of synchronization. First, simple filtering or searching with fashion tags on your website. Second, personalized recommendations of your products similar to the users’ images. The synchronization periodicity is up to you. This way the visually similar results on your web are always up to date with your actual SKUs.

How Does Fashion Search Work?

The Fashion Search works as a customizable recommendation service allowing you to easily build your own fashion product discovery system. Imagine you are building a watch shop with product recommendations based on material, dial colour, and type. You can add your custom tags and show customers products of specific colours and types.

The main added value of the Fashion Search lies in the possibility of combining advanced automatic Fashion Tagging with Visual Search. The technology behind our Visual Search is complex. It contains more than a hundred deep-learning models and algorithms for the extraction of dominant colours and other features. It also allows advanced filtering of visually similar results based on user-provided attributes.

Besides bringing these things together, there’s more we implemented to make the Fashion Search an effective and reliable tool tailored to the needs of fashion e-commerce. See how it works in our public demo.

Advanced Tagging with Object Detection

Object Detection is an indispensable tool for processing complex pictures with many items to recognize and tag. The service detects all fashion-related items on the picture and then provides tags for individual items.

We work with seven Top Categories (or types) of apparel: Clothing, Footwear, Watches, Bags, Accessories, Jewellery, and Underwear. Our system classifies the products from these categories into a hundred different categories, subcategories, and features with about 600 labels.

Customization of Fashion Search with Profiles

The most common questions we get from our customers in fashion are “What if I need something that is not included in the Fashion Tagging? “ and “What if I need to rename or translate the labels? “.

We always welcome customization and Fashion Search is not an exception. It enables our users to create a fully customized environment. Adding a new feature requires only a few hundred example images per tag for the system to recognize them automatically. Also, it is natural that everyone has their own taxonomy and preferred language. Even the naming can be different, such as Jacket vs. Coat in the USA and the UK. Renaming and changing the category structure is done using a custom fashion profile according to your needs.

Also, for specialized sellers, the requirements for the richness of labels are much higher if they want to bring the best product discovery experience. For example, a shop specializing in luxury jewellery needs to rename the tags for Colour (gold, golden, yellow gold, gold-coloured, rose gold) or add exclusive features and tags, such as the colour of the gemstones. If you like this approach, don´t hesitate to contact us.

Upgraded Fashion Taxonomy

Our fashion annotation team focuses on widening fashion taxonomy and training new machine learning algorithms to recognize new features daily. Categories such as Clothing, Accessories or Footwear are constantly growing, while others are being made. For instance, we recently added a new recognition model for Embellishment (e.g. embroidery or studs), rich sub-categories of Dresses, and new categories of Jewellery and Watches. In the last couple of months, we added over 50 new categories and attributes, improving the precision of tagging results.

We have a lot of discussions about what’s next to explore and expand – and that is where the customer’s ideas come into play. Check if you can find your categories in our Fashion Taxonomy.

Download the Fashion Taxonomy Sheet Here

Advanced Analysis and Sorting of Fashion Images

Some customers need to obtain more detailed information about their images. Features such as the presence of a human model, background quality, or perspective can be used for filtering and sorting. To enable deeper image analysis, we built a specialized Meta Tagging model, that can be used to:

• identify if there’s a person in the picture,
• filter images with white background,
• sort product images by the view,
• and many more.

This Visual AI model enables you to manage the display of product images and build a smoother customer experience. More importantly, it makes your web easier to understand and navigate with features such as:

• the first photo in the product list is always with/without a person,
• the next photos are sorted by perspective,
• the last photo is the detail, focused on the material, colour, or pattern,
• all images with low quality are filtered out,
• and so on.

This way, the visitors will always know what to expect and where to click to find what they’re looking for. For instance, it is important to display jewellery in standardised high-quality images with details, but also on the human body. Seeing a ring or a necklace on a hand or neck helps customers understand the fit and size of the jewellery, as well as the colours of materials and gems in natural light.

Community content also makes e-shopping sites more personal and engaging for visitors. A good example is Shein, where users can upload pictures along with their reviews of the product. This strategy definitely adds up to the trustworthiness and reliability of the seller.

How We Work with Data

Working with big specialized databases requires caution, patience, and a specific skill set. Our team of fashion annotators puts thousands of hours into building high-quality datasets with relevant labels and objects. To make their work more efficient and time well-spent, we developed our own image Annotation Tool, enhancing the functionalities of our App. It is the main reason why we deliver new features quickly, and it is available to our customers as well.

Each image shown to the model in training is verified numerous times by multiple annotators. Then, an optimisation mechanism makes sure we achieve the highest accuracy with the smallest amount of training images possible. After that, everything is evaluated on a broad test dataset. As a result, we only deploy models, that are inspected by Machine Learning specialists, and precise in their predictions.

Never-Ending Innovations

What else? There have been some significant improvements to our speed performance. Cooperating with the Intel AI team, we did a lot of work on our backend side so you can query the results in milliseconds. We firmly believe that our Fashion AI services are some of the best in the market. Covering a variety of items from Clothing, Footwear, or Accessories, our Fashion AI works with a very rich taxonomy. To sum up, we are now ready to focus on more new features. For example, mining relevant keywords from textual metadata, enhancing and upscaling product images with the Super-Resolution model, model explainability, or background removal, but also on better and richer customer experience.

Would you like to discuss your custom solution or ask anything? Read our story, check the pricing, or let’s talk!

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