The newest addition to our portfolio of solutions is the Comics Identification (/v2/comics_id). This service is designed to identify comics from images. While it’s still in the early stages, we are actively refining and enhancing its capabilities.

The API detects the largest comic book in an image, and provides key information such as the title, issue number, release date, publisher, origin date, and creator’s name, making it ideal for identifying comic books, magazines, as well as manga.

This tool is perfect for organizing and cataloging large comic collections, offering accurate identification and automation of metadata extraction. Whether you’re managing a digital archive or cataloging physical collections, the Comics Identification API streamlines the process by quickly delivering essential details. We’re committed to continuously improving this service to meet the evolving needs of comic identification.

Star Wars Unlimited, Digimon, Dragon Ball, and More Can Now Be Recognized by Our System

Our trading card identification system has already been widely used to accurately recognize and provide detailed information on cards from games like Pokémon, Yu-Gi-Oh!, Magic: The Gathering, One Piece, Flesh and Blood, MetaZoo, and Lorcana.

Recently, we’ve expanded the system to include cards from Garbage Pail Kids, Star Wars Unlimited, Digimon, Dragon Ball Super, Weiss Schwarz, and Union Arena. And we’re continually adding new games based on demand. For the full and up-to-date list of recognized games, check out our API documentation.

Detect and Identify Both Trading Cards and Their Slab Labels

The new endpoint slab_grade processes your list of image records to detect and identify cards and slab labels. It utilizes advanced image recognition to return detailed results, including the location of detected items and analyzed features.

The Slab Label object provides essential information, such as the company or category (e.g., BECKETT, CGC, PSA, SGC, MANA, ACE, TAG, Other), the card’s grade, and the side of the slab. This endpoint enhances our capability to categorize and assess trading cards with greater precision. In our App, you will find it under Collectibles Recognition: Slab Reading & Identification.

Automatic Recognition of Collectibles

Ximilar built an AI system for the detection, recognition and grading of collectibles. Check it out!

Read more

New Endpoint for Card Centering Analysis With Interactive Demo

Given a single image record, the centering endpoint returns the position of a card and performs centering analysis. You can also get a visualization of grading through the _clean_url_card and _exact_url_card fields.

The _tags field indicates if the card is autographed, its side, and type. Centering information is included in the card field of the record.

Learn How to Scan and Identify Trading Card Games in Bulk With Ximilar

Our new guide How To Scan And Identify Your Trading Cards With Ximilar AI explains how to use AI to streamline card processing with card scanners. It covers everything from setting up your scanner and running a Python script to analyzing results and integrating them into your website.

Let Us Know What You Think!

And that’s a wrap on our latest updates to the platform! We hope these new features might help your shop, website, or app grow traffic and gain an edge over the competition.

If you have any questions, feedback, or ideas on how you’d like to see the services evolve, we’d love to hear from you. We’re always open to suggestions because your input shapes the future of our platform. Your voice matters!

The post New AI Solutions for Card & Comic Book Collectors 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.

Sports Cards Collecting on The Rise

Collecting sports cards, including hockey cards, has been a popular hobby for many people. Especially during my childhood, I collected hockey cards, as a big fan of the sport. Today, card collecting has evolved into an investment, and many new collectors enter the community solely to buy and sell cards on various marketplaces.

Some traditional baseball rookie cards can have significant value, for example, the estimated price of a vintage Mickey Mantle PSA 10 1952 Topps rookie baseball card is $15 million – $30 million.

Our Existing Solutions for Card Collector Sites & Apps

Last year, we already released several services focused on trading cards:

• First, we released a Trading Card Game Identifier API. It can identify trading card games (TCGs), such as Pokémon, Magic The Gathering: MTG and Yu-Gi-Oh!, and more. We believe that this system is amongst the fastest, most precise and accurate in the world.

• Second, we built a Card Grading and fast Card Conditioning API for both sports and trading card games. This service can instantly evaluate each corner, edges, and surface, and check the centring in a card scan, screenshot or photo in a matter of seconds. Each of these features is graded independently, resulting in an overall grade. The outputs can be both values or conditions-based (eBay or TCGPlayer naming). You can test it here.

• We have also been building custom visual search engines for private collections of trading cards and other collectibles. With this feature, people can visit marketplaces or use their apps to upload card images, and effortlessly search for identical or similar items in their database with a click. Visual search is a standard AI-powered function in major price comparators. If a particular game is not on our list, or if you wish to search within your own collection, list, or portfolio of other collectibles (e.g., coins, stamps, or comic books), we can also create it for you – let us know.

We have been gradually establishing a track record of successful projects in the collectibles field. From the feedback of our customers, we hear that our services are much more precise than the competition. So a couple of months ago, we started building a sports card scanning system as well. It allows users to send the scan to the API, and get back precise identification of the card.

Our API is open to all developers, just sign up to Ximilar App, and you can start building your own great product on top of it!

Test it Now in Live Demo

This solution is already available for testing in our public demo. Try it for free now!

The Main Features of Sports Cards

There are several factors determining the value of the card:

• Rarity & Scarcity: Cards with limited production runs or those featuring star players are often worth more.

• Condition: Like any collectible item, the condition of a sports card is crucial. Cards in mint or near-mint condition are generally worth more than those with wear and tear.

• Grade & Grading services: Graded cards (from PSA or Beckett) typically have higher prices in the market.

• The fame of the player: Names of legends like Michael Jordan or Shohei Ohtani instantly add value to the trading cards in your collection.

• Autographs, memorabilia, and other features, that add to the card’s rarity.

Each card manufacturer must have legal rights and licensing agreements with the sports league, teams, or athletes. Right now, there are several main producers:

• Panini – This Italian company is the largest player in the market in terms of licensing agreements and number of releases.

• Topps – Topps is an American company with a long history. They are now releasing cards from Baseball, Basketball or MMA.

• Upper Deck – Upper Deck is a company with an exclusive license for hockey cards from the NHL.

• Futera – Futera focuses mostly on soccer cards.

Dozens of other card manufacturers were acquired by these few players. They add their brands or names as special sets in their releases. For example, the Fleer company was acquired by Upper Deck in 2005 and Donruss was bought by Panini.

Identifying Sports Cards With Artificial Intelligence

When it comes to sports cards, it’s crucial to recognize that the identification challenge is more complex than that of Pokémon or Magic The Gathering cards. While these games present challenges such as identical trading card artworks in multiple sets or different language variants, sports cards pose distinct difficulties in recognition and identification, such as:

• Amount of data/cards – The companies add a lot of new cards into their portfolio each year. As of the latest date, the total figure exceeds tens of millions of cards.

• Parallels, variations, and colours – The card can have multiple variants with different colours, borders, various foil effects, patterns, or even materials. More can be read in a great article by getcardbase.com. Look at the following example of the NBA’s LeBron James card, and some of its variants.

• Special cards: Short Print (SP) and Super Short Print (SSP) cards are intentionally produced in smaller quantities than the rest of the particular set. The most common special cards are Rookie cards (RC) that feature a player in their rookie season and that is why they hold sentimental and historical value.

• Serial numbered cards: A type of trading cards that have a unique serial number printed directly on the card itself.

• Authentic signature/autograph: These are usually official signature cards, signed by players. To examine the authenticity of the signature, and thus ensure the card’s value, reputable trading card companies may employ card authentication processes.

• Memorabilia: In the context of trading cards, memorabilia cards are special cards that feature a piece of an athlete’s equipment, such as a patch from a uniform, shoe, or bat. Sports memorabilia are typically more valuable because of their rarity. These cards are also called relic cards.

As you can see, it’s not easy to identify the card and its price and to keep track of all its different variants.

Example: Panini Prizm Football Cards

Take for example the 2022 Panini Prizm Football Cards and the parallel cards. Gold Prizms (10 cards) are worth much more than the Orange Prizms (with 250 cards) because of their scarcity. Upon the release of a card set, the accompanying checklist, presented as a population table, is typically made available. This provides detailed information about the count for each variation.

Next, for Panini Prizm, there are more than 20 parallel foil patterns like Speckle, Hyper, Diamond, Fast Break/Disco/No Huddle, Flash, Mozaic, Mojo, Pulsar, Shimmer, etc. with all possible combinations of colours such as green, blue, pink, purple, gold, and so on.

These combinations matter because some of them are more rare than others. There are also different names for the foil cards between companies. Topps has chrome Speckle patterns which are almost identical to the Panini Prizm Sparkle pattern.

Lastly, no database contains each picture for every card in the world. This makes visual search extremely hard for cards that have no picture on the internet.

Luckily, we developed (and are actively improving) an AI service that is trying to tackle the mentioned problems with sports cards identification. This service is available on click as an open REST API, so anyone can connect to develop and integrate their system with ours. The results are in seconds and it’s one of the fastest services available in the market.

How to Identify Sports Cards Via API?

In general, you can use and connect to the REST API with any programming language like Python or Javascript. Our developer’s documentation will serve you as a guide with many helpful instructions and tips.

To access our API, sign in Ximilar App to get your unique API authentication token. You will find the administration of your services under Collectibles Recognition. Here is an example REST Request via curl:

$ curl https://api.ximilar.com/collectibles/v2/sport_id -H "Content-Type: application/json" -H "Authorization: Token __API_TOKEN__" -d '{
    "records": [
        { "_url": "__PATH_TO_IMAGE_URL__"}
    ], "slab_id": false
}'

The API response will be as follows:

• When the system succesfuly indetifies the card, it will return you full identification. You will get a list of features such as the name of the player/person, the name of the set, card number, company, team and features like foil, autograph, colour and more. It is also able to generate URL links for eBay searches so you can check the card values or purchase them directly.
• If we are not sure about the identification (or we don’t have a specific card in our system) the system will return empty search results. In such case, feel free to ask for support.

How AI Sports Cards Identification Works?

Our identification system uses advanced machine learning models with smart algorithms for post-processing. The system is a complex flow of models that incorporates visual search. We trained the system on a large amount of data, curated by our own annotation team.

First, we identify the location of the card in your photo. Second, we do multiple AI analyses of the card to identify whether it has autograph and more. The third step is to find the card in our collection with visual search (reverse image search). Lastly, we use AI to rerank the results to make them as precise as possible.

What Sports Cards Can Ximilar Identify?

Our sports cards database contains a few million cards. Of course, this is just a small subset of all collectible cards that were produced. Right now we focus on 6 main domains: Baseball cards, Football cards, Basketball cards, Hockey cards, Soccer and MMA, and the list expands based on demand. We continually add more data and improve the system.

We try to track and include new releases every month. If you see that we are missing some cards and you have the collection, let us know. We can agree on adding them to training data and giving you a discount on API requests. Since we want to build the most accurate system for card identification in the world, we are always looking for ways to gather more cards and improve the software’s accuracy.

Who Will Benefit From AI-Powered Sports Cards Identifier?

Access to our REST API can improve your position in the market especially if:

• You own e-commerce sites/marketplaces that buy & sell cards – If you have your own shop, site or market for people who collect cards, this solution can boost your traffic and sales.

• You are planning to design and publish your own collector app and need an all-in-one API for the recognition and grading of cards.

• You want to manage, organize and add data to your own card collection.

Is My Data Safe?

Yes. First of all, we don’t save the analysed images. We don’t even have so much storage capacity to store each analysed image, photo, scan and screen you add to your collection. Once our system processes an image, it removes it from the memory. Also, GDPR applies to all photos that enter our system. Read more in our FAQs.

How Fast is the System, Can I Connect it to a Scanner?

The system can identify one card scan in one second. You can connect it to any card scanner available in the market. The scanning outputs the cards into the folders, to which you can apply a script for card identification.

Sports Cards Recognition Apps You Can Build With Our API

Here are a few ideas for apps that you can build with our Sport Card Identifier and REST API:

• Automatic card scanning system – create a simple script that will be connected to our API and your scanners like Fujitsu fi-8170. The system will be able to document your cards with incredible speed. Several of our customers are already organizing their collections of TCGs (like Magic The Gathering or Pokémon) and adding new cards on the go.

• Price checking app or portfolio analysis – create your phone app alternative to Ludex or CollX. Start documenting the cards by taking pictures and grading your trading card collection. Our system can provide card IDs, pre-grade cards, and search them in an online marketplace. Easily connect with other collectors, purchase & sell the cards. Test our system’s ability to provide URLs to marketplaces here.

• Analysing eBay submission – would you like to know what your card’s worth and how many are currently available in the market? For how much was the card sold in the past? Track the price of the card over time? Or what is the card population? With our technology, you can build a system that can analyse it.

AI for Trading Cards and Collectors

So this is our latest narrow AI service for the collector community. It is quite easy to integrate it into any system. You can use it for automatic documentation of your collection or simply to list your cards on online markets.

For more information, contact us via chat or contact page, and we can schedule a call with you and talk about the technical and business details. If you want to go straight and implement it, take look at our developer’s API documentation and don’t hesitate to ask for guidance anytime.

Right now we are also working on Comics identification (Comic book, magazines and manga). If you would like to hear more then just contact us via email or chat.

The post How to Identify Sports Cards With AI appeared first on Ximilar: Visual AI for Business.

When Visual Content Matters

About 90 % of information transmitted to the human brain is visual. There is no doubt that humans are fixated on visual information, with images and videos being the most popular content on the internet. According to Internet Live Stats, every second, more than 1 000 pictures are uploaded to Instagram and almost 100k videos are played on YouTube.

The more people love using and consuming visual content online, the more important visual merchandising gets. It is clear that if you want to increase the traffic and conversions on your website, you need to make your site and content as visually appealing as possible.

How Does Image Upscaling Work?

Image upscaling, or image enhancement, is a process in which images are enriched with more pixels to get a higher resolution. During this process, the image is divided into segments which are upscaled separately and then put back together. So, for example, during the 4x upscaling, the 64 x 64px segments turn into 256 x 256px.

The pixel multiplication is enabled by AI, using the techniques of deep learning and computer vision. During the training, the neural network learns how to divide each pixel into multiple pixels based on its surroundings. Some image enhancing techniques also involve generative modelling, which generates new information to make the modified image look convincing.

A Few Image Upscaling Examples

Super-resolution upscaling makes the edges and colour transitions smoother. When you find a perfect stock photo, you can increase the resolution by adding 2x, 4x, or 8x more pixels to the image.

Sometimes, the smallest changes to the image make the biggest difference. The upscaled images provide the feeling of greater depth and more details, and leave a better impression.

Where is The Image Enhancement the Most Needed?

Stock Photo Databases

The competition in the stock image market is enormous. Nowadays, users of paid stock photo databases expect combined visual search & search by tags, advanced filtering, high-quality photos, or even an editing interface.

When you implement an upscaling solution, you can level up your customer experience, ensure that images coming from thousands of authors will maintain a certain quality, or even make it a part of your own image editor.

Real Estate Photos

Real estate properties with great image galleries have a significantly greater chance of catching the attention of visitors and finding buyers faster.

If you have a collection of real estate images, you can use Custom Image Recognition to automatically choose the best pictures to be displayed, and then use the Image Upscaler to increase the resolution of images. To do so, you will need to train your categorization task first and then combine it with Image Upscaler via Flows.

E-Commerce

Online sellers usually receive their product pictures from various sources. That is why upscaler is a useful visual merchandising and product page optimization tool. You can add the image enhancement into your automatic image processing system to get a unified resolution for the product listing as well as the highest quality images for the product page.

This can also be done with Flows: you can create a task, which will choose all low-quality images and send them to an upscaling task. You can also combine this service with background removal or add it into a more complex Flow with tagging tasks.

Want to know more? Read how our AI helps online businesses.

Gaming

Upscaling technology is getting used in more and more industries, but the first super-resolution AI models were used in the gaming industry. For example, Xbox or your latest Nvidia GPU card can artificially increase the resolution of the game. Using image enhancement in games has several advantages:

• The rendering mechanism is used for creating low-resolution scenes and then a fast AI model is used to improve the resolution
• Older games, that are natively optimized for lower resolution, can be eventually played with improved graphics in a higher resolution

Generated Art

Generated images, artworks, and concept art are becoming increasingly popular with technologies such as Dall-E 2 and Midjourney. We tried our Generative (GAN) model on these photos and the results are amazing! You can get beautiful printable art in 4k or 8k resolution with our AI via API. What a time to be alive!

The Technology Behind Image Upscaler

Ximilar currently provides two image upscaling solutions: the Classic Image Upscaler and GAN Upscaler.

Classic Image Upscaler

The Classic Image Upscaler is based solely on pixel multiplication. It multiplies each pixel in an image 2–8 times to achieve a higher resolution without modifications to the image. The image upscaled by a classic upscaler is as true to the original image as possible. It is ideal if you only need to upscale your images without adding anything new. Typical examples are CCTV footage or images with delicate patterns and details, that should remain unchanged.

Ximilar is using the latest architecture of convolutional neural networks trained on high and low-quality images. The model outperforms the bicubic interpolation used in programs like Photoshop by several times.

Post-Processing Methods

The post-processing API can be used to remove unnecessary artifacts and noise from images (Artifact removal), focus on small details (High fidelity), or significantly smooth the entire image (Ironed out).

Each of these post-processing methods is good for different types of images. For example, smoothing is ideal for vector graphics or designs. Artifact removal is best for real-life images, e.g. family photos. High fidelity can be used in professional graphics.

GAN Image Upscaler

GAN Image Upscaler is a bit more advanced, and in fact, recommended upscaling technology, especially for commercial content. This upscaler analyzes the colors, edges, corners, light and shade in the original image and enhances its resolution by generating new pixels, that are as relevant pixels as possible to make the resulting images natural-looking. It makes stock photos and product images look more appealing.

How to Upscale an Image Using the Image Upscaler?

A lot of smartphone apps use upscaling models to improve user photos. Brands such as iPhone or Huawei include enhancing models in their software. These models are hidden from the eyes of the user and participate in making photos. Our super-resolution model can be used anywhere simply by calling the Rest API.

Synchronous and Asynchronous API Requests

A basic upscaling task uses synchronous request, meaning you upload an image, wait for it to be processed and eventually get the upscaled result. Synchronous API requests are typically used in public upscaling tools and are currently set for testing purposes in our App. They can however be ineffective for companies that upscale large volumes of data at once and want to keep track of the progress.

That is why we also provide and recommend an API endpoint for asynchronous requests. The difference is that you send multiple upscaling requests (specified by id), they are queued and then processed one by one. You can also send other requests to track the progress of the job. We especially recommend this approach if you need to upscale whole databases, e.g. e-shops with large product photo collections or stock photo databases. You can also use Webhook and get a notification once the job is done.

The model is accessible via the following async API endpoints:

https://api.ximilar.com/account/v2/request

You can also test upscaling of images in Ximilar App (with the option to use the latest GAN model).

Image Upscaler at Stockphotos.com

The Image Upscaler by Ximilar is used at one of the best-known stock photo banks, StockPhotos. The service is free of charge for testing purposes.

Would you like to implement an AI image upscaler into your own app or system? Feel free to contact us anytime.

The post Image Upscaler: API for Super-Resolution Image Enhancing 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.

How API Credits Work

Imagine you upload a training image, create a recognition label, or send an image for recognition in our App. Every time you perform an operation like this, you send a request to our server using API. This request is called an API call.

To keep track of API calls and their requirements, each type of call corresponds to a certain number of API credits. Generally, all calls sending image data to our servers cost some API credits. The full list of operations with their API credit values is available in our documentation.

Your Monthly API Credits

Every user of the Ximilar App is provided with a monthly supply of API credits, depending on their pricing plan. This supply is renewed every month on the day they made the purchase of their plan. For example, if you purchase a Business plan on April 15th, your monthly supply will be restored on the 15th day of every consequent month.

The users with the Free pricing plan are provided with a monthly supply of API credits as well. Whether you use a paid or free plan, the unused API credits from your monthly supply are not transferred to the following month and expire.

Introducing API Credit Packs

Ximilar App users can now buy an unlimited number of API credits aside from their monthly supply, in the form of API credit packs. This option is available for all pricing plans, including the Free plan.

There are two major benefits of the API credit packs. First, credits from the packs are used only when your monthly supply of credits runs out. In this example, the user with the Business plan has already used all API credits from his monthly supply and the system automatically switched to using the API credit pack. On April 15th, his monthly credit balance will be renewed, and the system will switch back to the monthly supply.

Second, API credit packs have no expiration. Therefore, their balance passes to the next month. You can buy as many credit packs as you need. The credits will add up in the lower API credit bar.

Typical Uses for API Credit Packs

The credit packs cover both expected and unexpected system loads. There are more ways and situations in which they can help or serve as safety nets.

Get Your System Ready

Our users generally pick their pricing plan based on regular traffic on their websites. However, the initial service setup is more demanding, and it costs a lot of extra credits. In this case, you wouldn’t want to upgrade your pricing plan for the short period of higher workloads and then downgrade back to the plan suiting your long-term needs.

One-Time System Loads

As you could see in the example with a Business plan user, the number of API credits in the credit pack bar was twice as high as his monthly credit supply. It is common for our users to use an above-average number of credits from time to time – typically when they are expecting higher system loads than usual. For example, uploading more products and images, or adding a brand new collection, would mean withdrawing your monthly credit supply too soon. In such cases, API credit packs provide a cost-effective solution.

Safety Net in a Case of Higher Traffic

The credit packs also cover the situations of unpredicted system loads caused by third parties. For example, when your website is visited and the system is used by an unexpected number of customers in a short period.

This way, the credit packs provide a sort of safety net to make sure no service outages will occur on your side due to the sudden exhaustion of credits.

What if I Upgrade or Downgrade My Plan?

You can always upgrade or downgrade your pricing plan. When this happens, the credits from your previous plan’s monthly supply will add up to the monthly supply of your new plan. They will remain in the bar till the end of your old monthly subscription and will be used first. In addition, you can purchase as many credit packs as you need, and the credits from the packs will be used after both of your monthly supplies are exhausted.

Do you have any questions? We’re more than happy to talk.

The post Ximilar Introduces API Credit Packs 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!

The post Everything You Need to Know About Fashion Search appeared first on Ximilar: Visual AI for Business.

AI Adoption by Companies Worldwide

Let’s start with some interesting stats and news. The expansion of AI and Machine Learning is becoming common across numerous industries. According to this report by Stanford University, AI adoption is increasing globally. More than 50 % of respondents said their companies were using AI, and the adoption growth was greatest in the Asia-Pacific region. Some people refer to the automation of factory processes, including digitalization and the use of AI, as the Fourth Industrial Revolution (and so-called Industry 4.0).

The data show that the Automotive industry is the largest adopter of AI in manufacturing, using heavily machine learning, computer vision, and robotics.
Other industries, such as Pharma or Infrastructure, are using computer vision in their production lines as well. Financial services, on the other hand, are using AI mostly in operations, marketing & sales (with a focus on Natural Language Processing – NLP).

The MIT Technology Review cited the statement of a leading artificial intelligence expert Andrew Ng, who has been helping tech giants like Google implement AI solutions, that factories are AI’s next frontier. For example, while it would be difficult to inspect parts of electronic devices with our eyes, a cheap camera of the latest Android or iPhone can provide high-resolution images that can be connected to any industrial system.

Adopting AI brings major advantages, but also potential risks that need to be mitigated. It is no surprise that companies are mainly concerned about the cybersecurity of such systems. Imagine you could lose a billion dollars if your factory stopped working (like Honda in this case). Other obstacles are potential errors in machine learning models. There are techniques on how to discover such errors, such as the explainability of AI systems. As for now, the explainability of AI is a concern of only 19 % of companies so there is space to improve. Getting insight from the algorithms can improve the processes and quality of the products. Other than security, there are also political & ethical questions (e.g., job replacement or privacy) that companies are worried about.

This survey by McKinsey & Company brings interesting insights into Germany’s industrial sector. It demonstrates the potential of AI for German companies in eight use cases, one of which is automated quality testing. The expected benefit is a 50% productivity increase due to AI-based automation. Needless to say, Germany is a bit ahead with the AI implementation strategy – there are already several plans made by German institutions to create standardised AI systems that will have better interoperability, certain security standards, quality criteria, and test procedures.

Highly developed economies like Germany, with a high GDP per capita and challenges such as a quickly ageing population, will increasingly need to rely on automation based on AI to achieve GDP targets.

McKinsey & Company

Another study by PwC predicts that the total expected economic impact of AI in the period until 2030 will be about $15.7 trillion. The greatest economic gains from AI are expected in China (26% higher GDP in 2030) and North America.

What is Visual Quality Control?

The human visual system is naturally very selective in what it perceives, focusing on one thing at a time and not actually seeing the whole image (direct vs. peripheral view). The cameras, on the other hand, see all the details, and with the highest resolution possible. Therefore, stories like The Big Hack show us the importance of visual control not only to ensure quality but also safety. That is why several companies and universities decided to develop optical inspection systems engaging machine learning methods able to detect the tiniest difference from the reference board.

In general, visual quality control is a method or process to inspect equipment or structures to discover defects, damages, missing parts, or other irregularities in production or manufacturing. It is an important method of confirming the quality and safety of manufactured products. Optical inspection systems are mostly used for visual quality control in factories and assembly lines, where the control would be hard or ineffective with human workers.

What Are the Main Benefits of Automatic Visual Inspection?

Here are some of the essential aspects and reasons, why automatic visual inspection brings a major advantage to businesses:

• The human eye is imprecise – Even though our visual system is a magnificent thing, it needs a lot of “optimization” to be effective, making it prone to optical illusions. The focused view can miss many details, our visible spectrum is limited (380–750 nm), and therefore unable to capture NIR wavelength (source). Cameras and computer systems, on the other hand, can be calibrated to different conditions. Cameras are more suitable for highly precise analyses.
• Manual checking – Manual checking of the items one by one is a time-consuming process. Smart automation allows processing and checking more items and faster. It also reduces the number of defective items that are released to customers.
• The complexity – Some assembly lines can produce thousands of various products of different shapes, colours, and materials. For humans, it can be very difficult to keep track of all possible variations.
• Quality – Providing better and higher quality products by reducing defective items and getting insights into the critical parts of the assembly line.
• Risk of damage – Machine vision can reduce the risk of item damage and contamination by a person.
• Workplace safety – Making the work environment safer by inspecting it for potentially dangerous actions (e.g. detection of protection wearables as safety helmets in construction sites), inspection in radioactive or biohazard environments, detection of fire, covid face masks, and many more.
• Saving costs – Labour work can be pretty expensive in the Western world.
  For example, the average Quality control inspector salary in the US is about 40k USD. Companies consider numerous options when saving costs, such as moving the factories to other countries, streamlining the operations, or replacing the workers with robots. And as I said before, this goes hand in hand with some political & ethical questions. I think the most reasonable solution in the long term is the cooperation of workers with robotic systems. This will make the process more robust, reliable, and effective.
• Costs of AI systems – Sooner or later, modern technology and automation will be common in all companies (Startups as well as enterprise companies). The adoption of automatic solutions based on AI will make the transition more affordable.

Where is Visual Quality Control Used?

Let’s take a look at some of the fields where the AI visual control helps:

• Cosmetics – Inspection of beauty products for defects and contaminations, colour & shape checks, controlling glass or plastic tubes for cleanliness and rejecting scratched pieces.
• Pharma & Medical – Visual inspection for pharmaceuticals: rejecting defective and unfilled capsules or tablets or the filling level of bottles, checking the integrity of items; or surface imperfections of medical devices. High-resolution recognition of materials.
• Food Industry and Agriculture – Food and beverage inspection for freshness. Label print/barcode/QR code control of presence or position.

A great example of industrial IoT is this story about a Japanese cucumber farmer who developed a monitoring system for quality check with deep learning and TensorFlow.

• Automotive – Examination of forged metallic parts, plastic parts, cracks, stains or scratches in the paint coating, and other surface and material imperfections. Monitoring quality of automotive parts (tires, car seats, panels, gears) over time. Engine monitoring and predictive autonomous maintenance.
• Aerospace – Checking for the presence and quality of critical components and material, spotting the defective parts, discarding them, and therefore making the products more reliable.
• Transportation – Rail surface defects control (example), aircraft maintenance check, or baggage screening in airports – all of them require some kind of visual inspection.
• Retail/Consumer Goods & Fashion – Checking assembly line items made of plastics, polymers, wood, and textile, and packaging. Visual quality control can be deployed for the manufacturing process of the goods. Sorting imprecise products.
• Energy, Mining & Heavy Industries – Detecting cracks and damage in wind blades or solar panels, visual control in nuclear power plants, and many more.

It’s interesting to see that more and more companies choose collaborative platforms such as Kaggle to solve specific problems. In 2019, the contest by Russian company Severstal on Kaggle led to tens of solutions for the steel defect detection problem.

Steel defects [Source: Kaggle]

• Other, e.g. safety checks – if people are present in specific zones of the factory if they have helmets, or stopping the robotic arm if a worker is located nearby.

The Technology Behind AI Quality Control

There are several different approaches and technologies that can be used for visual inspection on production lines. The most common nowadays are using some kind of neural network model.

Neural Networks – Deep Learning

Neural Networks (NN) are computational models that accept the input data and output relevant information. To make the neural network useful (finding the weights for the connection between the neurons and layers), we need to feed the network with some initial training data.

The advantage of using neural networks is their power to internally represent training data which leads to the best performance compared to other machine learning models in computer vision. However, it brings challenges, such as computational demands, overfitting, and others.

[Un|Semi|Self] Supervised Learning

If a machine-learning algorithm (NN) requires ground truth labels, i.e. annotations, then we are talking about supervised learning. If not, then it is an unsupervised method or something in between – semi or self-supervised method. However, building an annotated dataset is much more expensive than simply obtaining data with no labels. The good news is that the latest research in Neural Networks tackles problems with unsupervised learning.

Here is the list of common services and techniques for visual inspection:

• Image Recognition – Simple neural network that can be trained for categorization or error detection on products from images. The most common architectures are based on convolution (CNN).
• Object Detection – Model able to predict the exact position (bounding box) of specific parts. Suitable for defect localization and counting.
• Segmentation – More complex than object detection, image segmentation can tell you a pixel-based prediction.
• Image Regression – Regress/get a single decimal value from the image. For example, getting the level of wear out of the item.
• Anomaly Detection – Shows which image contains an anomaly and why. Mostly done by GAN or GRAD-CAM.
• OCR – Optical Character Recognition is used for getting and reading text from images.
• Image matching – Matching the picture of the product to the reference image and displaying the difference.
• Other – There are also other solutions that do not require data at all, most of the time using some simple, yet powerful computer vision technique.

If you would like to dive a bit deeper into the process of building a model, you can check my posts on Medium, such as How to detect defects on images.

Typical Types and Sources of Data for Visual Inspection

Common Data Sources

RGB images – The most common data type and the easiest to get. A simple 1080p camera that you can connect to Raspberry Pi costs about 25$.

Thermography – Thermal quality control via infrared cameras, mostly used to detect flaws not visible by simple RGB cameras under the surface, gas imaging, fire prevention, and electronics behaviour under different conditions. If you want to know more, I recommend reading the articles in Quality Magazine.

3D scanning, Lasers, X-ray, and CT scans – Creating 3D models from special depth scanners gives you a better insight into material composition, surface, shape, and depth.

Microscopy – Due to the rapid development and miniaturization of technologies, sometimes we need a more detailed and precise view. Microscopes can be used in an industrial setting to ensure the best quality and safety of products. Microscopy is used for visual inspection in many fields, including material sciences and industry (stress fractures), nanotechnology (nanomaterial structure), or biology & medicine. There are many microscopy methods to choose from, such as stereomicroscopy, electron microscopy, opto-digital or purely digital microscopes, and others.

Common Inspection Errors

• scratches
• patches
• knots, shakes, checks, and splits in the wood
• crazing
• pitted surface
• missing parts
• label/print damage
• corrosion
• coating nonuniformity

Examples of Datasets for Visual Inspection

• Severstal Kaggle Dataset – A competition for the detection of defects on flat sheet steel.
• MVTec AD – 5000 high-resolution annotated images of 15 items (divided into defective and defect-free categories).
• Casting Dataset – Casting is a manufacturing process in which a liquid material is usually poured into a form/mould. About 7 thousand images of submersible pump defects.
• Kolektor Surface-Defect Dataset – Dataset of microscopic fractions or cracks in electrical accumulators.
• PCB Dataset – Annotated images of printed circuit boards.

AI Quality Control Use Cases

We talked about a wide range of applications for visual control with AI and machine learning. Here are three of our use cases for industrial image recognition we worked on in 2020. All these cases required an automatic optical inspection (AOI) and partial customization when building the model, working with different types of data and deployment (cloud/on-premise instance/smartphone). We are glad to hear that during the COVID-19 pandemic, our technologies help customers keep their factories open.

Our typical workflow for a customized solution is the following:

1. Setup, Research & Plan: If we don’t know how to solve the problem from the initial call, our Machine Learning team does the research and finds the optimal solution for you.
2. Gathering Data: We sit with your team and discuss what kind of data samples we need. If you can’t acquire and annotate data yourself, our team of annotators will work on obtaining a training dataset.
3. First prototype: Within 2–4 weeks we prepare the first prototype or proof of concept. The proof of concept is a lightweight solution for your problem. You can test it and evaluate it by yourself.
4. Development: Once you are satisfied with the prototype results, our team can focus on the development of the full solution. We work mostly in an iterative way improving the model and obtaining more data if needed.
5. Evaluation & Deployment: If the system performs well and meets the criteria set up in the first calls (mostly some evaluation on the test dataset and speed performance), we work on the deployment. It can be used in our cloud, on-premise, or embedded hardware in the factory. It’s up to you. We can even provide a source code so your team can edit it in the future.

Use case: Image recognition & OCR for wood products

One of our customers contacted us with a request to build a system for categorization and quality control of wooden products. With Ximilar Platform we were able to easily develop and deploy a camera system over the assembly line that sorted the products into the bins. The system can identify the defective print on the products with optical character recognition technology (OCR), and the surface control of wood texture is enabled by a separate model.

The technology is connected to a simple smartphone/tablet camera in the factory and can handle tens of products per second. This way, our customer was able to reduce rework and manual inspections which led to saving thousands of USD per year. This system was built with the Ximilar Flows service.

Use case: Spectrogram analysis from car engines

Another project we successfully deployed was the detection of malfunctioning engines. We did it by transforming the sound input from the car into an image spectrogram. After that, we train a deep neural network that recognises problematic car engines and can tell you the specific problem of the engine.

The good news is that this system can also detect anomalies in an unsupervised way (no need for data labelling) with the GAN technology.

Use case: Wind Turbin Blade damages from drone footage

According to Bloomberg, there is no simple way to recycle a wind turbine, and it is therefore crucial to prolong the lifespan of wind power plants. They can be hit by lightning, influenced by extreme weather, and other natural forces.

That’s why we developed for our customers a system checking the rotor blade integrity and damages working with drone video footage. The videos are uploaded to the system, and inspection is done with an object detection model identifying potential problems. There are thousands of videos analyzed in one batch, so we built a workstation (with NVidia RTX GPU cards) able to handle such a load.

Ximilar Advantages in Visual AI Quality Control

• An end-to-end and easy-to-use platform for Computer Vision and Machine Learning, with enterprise-ready features.
• Processing hundreds of images per second on an average computer.
• Train your model in the cloud and use it offline in your factory without an internet connection. Thanks to TensorFlow, you can use the model on any computer, edge device, GPU card, or embedded hardware (Raspberry Pi or NVIDIA Jetson connected to a camera). We also provide optimized CPU models on Intel devices through OpenVINO technology.
• Easily gather more data and teach models on new defects within a day.
• Evaluation of the independent dataset, and model versioning.
• A customized yet affordable solution providing the best outcome with pixel-accurate recognition.
• Advanced image management and annotation platform suitable for creating intelligent vision systems.
• Image augmentation settings that can be tuned for your problem.
• Fast machine learning models that can be connected to your industrial camera or smartphone for industrial image processing robust to lighting conditions, object motion, or vibrations.
• Great team of experts, available to communicate and help.

To sum up, it is clear that artificial intelligence and machine learning are becoming common in the majority of industries working with automation, digital data, and quality or safety control. Machine learning definitely has a lot to offer to the factories with both manual and robotic assembly lines, or even fully automated production, but also to various specialized fields, such as material sciences, pharmaceutical, and medical industry.

Are you interested in creating your own visual control system?

The post Visual AI Takes Quality Control to a New Level appeared first on Ximilar: Visual AI for Business.

Upcoming Changes in the EU Energy Labelling

The energy labels indicate the energy efficiency category the appliance falls into. In 2019, the European Union approved a new regulation setting a framework for updated energy labelling, which will come into force in 2021 and gradually replace the old system of labels. According to European lawmakers, the new system could save up to 200 billion kWh of energy, which is approximately the same amount of energy all Baltic countries spend together in a year. The first new labels are already in circulation.

Effective March 2021, sellers and manufacturers will be required to update the energy labels on fridges, washing machines, dishwashers, TVs, electronic displays, and refrigerating appliances for display purposes, followed by tyres in May, and lamps in September.

So far, the products have fallen into categories A+++ to G, which will be simplified back to A to G and the energy class of a product will be determined by higher standards. This means the appliance that was A+ in 2020 could be B or C from now on.

Re-scaling is not the only new feature, as the new labels are provided with a QR code leading consumers to the EPREL (European Product Registry for Energy Labelling) database, providing them with detailed energy and environmental information on the goods.

A Challenge for E-commerce Industry

The new regulation applies not only to retail but also to e-commerce, meaning all e-shops will be required to re-label the household appliances as well. They will be required to do so between March 1^st and 18^th.

E-shops need to identify thousands of energy labels in the product galleries and replace them with the new ones.

E-shops generally upload the energy labels as pictures into the galleries on the item pages. Due to the large amounts of images they upload every day, it is not uncommon not to have them tagged.

To ensure a smooth transition from the old label system to the new one, the physical stores will focus on the re-labelling of the displayed goods. The e-shops, on the other hand, will need to identify and replace considerable amounts of pictures in their databases at once. For instance, the largest e-shop selling household appliances in the Czech Republic Alza.cz currently offers approximately 1 200 products in the category of fridges, 500 in washing machines, 350 in dishwashers, 600 TVs, and 1 200 monitors, meaning they will need to update at least 3 850 energy labels in the first wave.

Many large e-shops also cooperate with price comparison websites, such as Heureka, that have their item galleries. For such services, the problem is a bit more complex: as a price analysis tool, the comparison website acquires its data from various sellers meaning its picture tagging or sorting is not standardised, and they have to deal with a wide range of file types and names.

Such task poses a question: what is the most efficient way to identify the old energy labels amongst other images in the product galleries in order to delete and replace them? The solution lies in the image recognition software.

Smart Solution: Image Recognition

E-shops with electronics typically upload the energy labels as images into the product galleries on their item pages and provide them to the price comparison websites. Therefore, they need software able to sort the product images, reliably recognize the old energy labels and set them aside.

Image Recognition is one of the core services of Ximilar. In principle, once you upload your images to this service, it equips them with tags and sorts them into categories. This service uses computer vision and deep learning to detect a wide range of features in the pictures. It is designed to process extensive databases of pictures in a fraction of a second.

With Ximilar App, you can develop an AI service directly for energy label recognition.

How to Use the Image Recognition on Energy Labels

If you need to identify and replace the old energy labels in your e-shop, there are two ways to use the Ximilar Energy Label Recognition service:

1. You can train your own recognition model for energy-label images. Then you can use the model as an API endpoint. Meaning, you will send images from the product gallery and get immediate feedback on whether they are or aren’t energy labels.
2. You can provide us with an export from your product image database (as image URLs or the actual files) and we will take care of the rest for you. You will get the output back in a standard CSV format.

Since image recognition is a CPU/GPU-intensive process, one of the greatest advantages of this service lies in the image database processing on our servers, whether you use the API or leave it to us. Of course, you will have a chance to test the service in the Ximilar App before you run it on your image database.

The energy label recognition with the Ximilar service is an efficient, quick, and above all, reliable way to identify the images that need to be replaced.

With Ximilar you can develop more models for energy labels recognition:

1. Reliable recognition of the old energy labels from the new ones. This might be handy in the transition period when some labels will be already replaced, but others will not.
2. Reading the actual energy class, especially from the new energy labels. The energy label change is a great opportunity to enrich your product data by this piece of information.

If you are interested, please just fill out our contact form. We are here to help!

The Image Recognition Service Makes E-commerce Easier

Whether you need to sort your catalogue into fine-grained categories, recognize pictures in product galleries, or offer similar products to your customers, Ximilar has a solution for you.

Read more in this detailed article on Image Recognition uses in e-commerce, or contact us, and we can discuss other solutions tailored to your needs.

The post Image Recognition as an Answer to New Energy Labelling appeared first on Ximilar: Visual AI for Business.