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.

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

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

Enhanced Taxonomy for Accessories Product Tagging

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

Automate Detection & Tagging of Home Decor Images With AI

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

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

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

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

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

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

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

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

We’ll look at companies developing interesting technologies for sports card recognition. Online card grading solutions (both offline and online, with AI and human graders), marketplaces where you can list cards, scanner companies that automate identification in big warehouses, mobile apps for managing and valuing personal collections, platforms for card investors, and special vaults for storing precious items can all benefit from automated sports card recognition.

Scanners for Sports Card Recognition

Card scanners are becoming very popular and there are tens of companies around the world with different pros and cons, features, capacity, processing speed, and pricing or subscription plans. All of them want to solve the problem of unmanaged warehouses full of cards.

TCG Sync – TCGSync is a very interesting startup that offers tools for cards like scanners (in partnership with Fujitsu), card catalogizing, inventory management, auto pricing and more. They support a lot of TCG types and have their own shop for card scanners that are ready for use. If you sign up for their yearly plan they will even give you a Fujitsu scanner for free and you can start selling on eBay, Shopify or Card Market from day 1.

If you already have a scanner and want to just identify the cards, you can connect to our API service with tools that will do it for you. Read more in my article How to Identify Sports Cards With AI.

Card Dealer Pro – This site is very similar to TCG Sync, but focused on sports cards. You just feed the cards to your scanner, Card Dealer will identify them with AI, propose a price for listing with title and description and publish them to eBay, Shopify or CollX.

Krono cards – This tool by Kronozio is for scanning and documenting your card inventory in bulk. It’s very similar to the TCG Sync and Card Dealer Pro. However, when you scan a card with a Krono Card, they directly submit it to their own marketplace. This can be a great advantage if you don’t have your own shop and a disadvantage if you don’t want actually to populate their database.

These are three main, but you can also explore several other options. For instance, TCG Machines is a Canadian company providing its own machine. Roca Sorter by TCG Player focuses on the four main TCGs: Magic the Gathering, Yu-Gi-Oh!, Pokémon, and Lorcana. CardCastle is an Australian company with its own scanner and platform to organize your collection. SortSwift is a system for managing your hobby store using the Ricoh scanner.

Sports Card Recognition & Evaluation With Smartphone Apps

Smartphone apps are really useful when you want to just check the card, its condition, or price on the internet. Here are the most popular ones that millions of collectors around the world use daily.

Ludex – A simple card scanner app that helps you identify the sports cards and get the prices using sports card recognition. After that, you can list it on eBay with a few clicks. In their free plan, you can scan up to 200 cards monthly. The plans are currently for 4.99, 9.99 and 24.99 USD enabling more functionalities like customized collections.

CollX – CollX is very similar to Ludex. With this smartphone application, you can snap a photo of your card and get its value in seconds. It’s the most popular smartphone app for card recognition and collection management. Also, the community is pretty active and you can easily submit a card to their own marketplace. It can tell the actual marketplace value and find similar listings.

Cardstock: Price Sports Cards: This app by Cardstock helps you with the identification and valuing of your cards. It’s designed for iPad and it can analyze baseball cards with great accuracy. This app is great for individual collectors, go and try it!

Collectr – Collectr is a great application for TCGs, that updates the value of the cards daily. You can manage your inventory and see the total invested value in the cards. I’m using the APP myself and the card scanning technology works great. My portfolio of cards is growing on value – it’s quite addictive

Sports Card Investor – It’s a great website with a smartphone app for everyone interested in investing in sports cards. You can see which cards are trending, you can search cards by complex queries, view recent sale prices and look at how the card is trending over the past. There are a lot of articles, resources, tips and also a very active community on Discord and social networks.

PSA Set Registry – This enables tracking your inventory of PSA-graded cards, seeing the populations and updating your own sets/collections. Basically, it is a gamification of collecting: you can compare your collection to others and compete on the leaderboard with your cards or get achievements or awards for collectors.

Online and Offline Card Grading Services

Card Grading is submitting and evaluating the quality and origin of the cards by third-party service with final sealing cards to the slabs. The grading usually increases the value of the cards (by demand) and makes the cards protected.

PSA, Beckett & CGC – There are several standard grading companies and the most popular are the PSA, Beckett and CGC. The PSA has the largest market share and the cards they grade generally have higher value than those from other companies. CGC first started with the comics but they are also doing cards now with currently refreshed labels.

Ace Grading – Ace Grading is a company from the UK with really cool slab labels. At this moment, they focus mostly on TCGs like Pokemons, Lorcana, or Magic The Gathering. This is a very good option for non-US citizens. The pricing is transparent with great support.

Tag Grading – Sometimes human graders can make a big mistake or can be very subjective during the grading process. Submitting to standard companies like PSA or Beckett can be very shady. That is why some companies try to develop grading based on computer vision. TAG Grading is a startup that develops its own technology for card grading. They use a scanner and AI models that can grade a card with accuracy, transparency and consistency. When TAG grades your card, you also get the grading report with an explanation of the grade. I think this is the way the grading should be done in future.

There are several online tools that you can use as individual collectors. For example, EdgeGrading provides a great web tool for getting the centering score. You simply upload the image of your card and adjust the Left / Right and Top / Bottom offsets.

SportscardsPro is also offering a centring web-based tool. However, the card photo cannot be scanned and there must be some background around the card. TrueGrade on the other hand is a smartphone app that grades cards based on the evaluation of centering, corners, edges and surfaces. A website alternative to TrueGrade is TCGrader – an AI-powered Pokemon grading system.

We built several useful tools for AI-powered sports card recognition, trading card game identification, card grading, and search. Read more in the articles and let me know what you think.

Online Platforms for Collectors

There are several platforms that can help you manage your collection, connect with the community, price the cards, sell them and much more.

Card Ladder – Card Ladder (by collectors.com) is a great platform for finding the value of your cards – including historical prices from several marketplaces like eBay, Goldin, Heritage or MySlabs and population reports. It also offers complex analytics and can track your collection. Personally, I like features like the charts of historical prices for my collection or notifications (price alerts) when some cards hit specified prices. They offer a free trial. Then, you need to subscribe for 15 USD/Month.

Price Charting – In my opinion, this is an awesome website, not only for trading cards but for comic books, video games, lego sets and coins as well. It offers you a search-by-photo functionality for selected card types/games. Our favourite function is an API for finding the value of ungraded or graded cards. The value for graded cards can be categorized per grade which many collectors consider to be the best source for price identification.

Collectibles.com – Collectibles are a quite new project with several interesting features. They have their own mobile application for iOS and Android. You can create your collection of cards, coins, stamps, memorabilia or comic books (with a showcase feature). The mobile app can scan and identify the items via AI-enhanced image recognition and add them to your collection. You can track the value of your collection so you can get better insights. Moreover, it has an active community where you can connect with other collectors, which is a big plus.

Collectors.com – Collectors are currently one of the largest market players (it is a site of Collectors Holdings, Inc.) when it comes to sports cards and trading card games. It has several divisions, one of them is a popular card grading company PSA. It also acquired companies like Wata Games. Currently, their app helps with managing your collections, selling them or sending them to PSA Vault. The PSA Vault is a cool service that helps you securely store your collector’s items with the opportunity to publicly sell them on their marketplace or on eBay.

Cardbase – Lastly, Cardbase is a platform designed for trading card enthusiasts to search, discover, and manage their collections. It aggregates prices and availability from over 30 marketplaces and auction houses, allowing users to track card values, view price trends, and find deals. Key features include comprehensive collection management tools, price tracking, and a mobile app for on-the-go access. Additionally, the site provides useful articles, guides, and resources for collectors.

Collectibles Marketplaces

If you are looking to sell your cards there are big sites as well as smaller ones, specialising in sports cards and TCGs. In general, you can always sell your card on eBay, but if you have a really expensive card then maybe you can try some auction house.

Sportlots.com – This is an amazing marketplace where you can get low-end sports cards very cheaply. In total, it lists more than 80 million cards from over 1000 sellers. The website has a kind of 90’s vibe but it has a lot of reputable sellers. Also, you can save a lot with their box system. That means that during the checkout, you can ship cards to your personal box and once you gather a good amount of cards you can ship them at once.

COMC.com – Check Out My Collectibles is a large marketplace and auction for all the card types. If you have more than a few hundred sports cards you should probably try to sell them via this marketplace.

Goldin.co – Goldin is a well-known auction house (goldin was acquired by eBay) that specializes in sports memorabilia, trading cards and sports cards. They are hosting high-profile auctions featuring rare and valuable items. The site is so popular that the founder Ken Goldin was featured in their own Netflix series King of Collectibles, The Goldin Touch. The Goldin is also a marketplace with tens of thousands of listings. Similar to the Goldin marketplace there is also PWCC which offers auctions, vaults and a marketplace.

The Most Popular Marketplaces

Cardmarket – This is originally a German company that offers a marketplace for your cards. It’s also the most popular marketplace in Europe. Just sign up and you can sell your singles, booster boxes or sealed products in minutes. It is very similar to eBay (each seller has a profile with reviews) but specialized in games like Pokemon, Dragon Ball, One Piece and others.

TCG Player – This is also one of the most popular marketplaces for selling trading card games (seller accounts), their marketplace supports a large number of games. The site was acquired by eBay in 2022. It has a lot of features, a mobile app, inventory management, and great customer support. They also offer developer tools like API for knowing the price of the card.

Japanese Card Marketplaces

In some cases, the Japanese sites can be very useful because cards are very popular in Japan and it’s a big market for sellers and buyers. So I picked a few that you should check out.

Cardotaku.com – is quite a great site developed for getting Japanese variations of cards. Started as a one-man business, and its popularity is growing. For the Japanese version of Magic The Gathering, we recommend checking Hareruya and Bigweb.

On TCG Republic you can find cards from various games. In general, I would recommend also checking out classic eBay and Mercari.com with their trading cards and collectibles sections.

E-Commerce Platforms

Do you need your own e-commerce solution with inventory management and many other features? Then try one of these platforms.

BinderPOS – BinderPOS is a solution that can run on top of your Shopify store and help you with the collectibles inventory. Originally from New Zealand, it quickly raised popularity among game stores worldwide.

CrystalCommerce – This is an in-store & online e-commerce platform for collectibles. A very similar solution to BinderPOS which helps you sell the stuff to several sales channels (such as eBay, Amazon, TCGplayer, and others). It’s easy to set up and you can pick from several website themes.

Storepass – Storepass is marketed as software for board games and TCG stores. It’s a generic platform on top of your e-commerce site like Shopify or BigCommerce. You can automatically access TCG market prices from TCG Player, manage your product inventory, edit the cards in bulk, and much more.

Other Projects

Lastly, I want to mention several other interesting projects, which do not offer typical services but can be very helpful for individual collectors.

For Card Pricing & Shipment

Mavin.io and Card Mavin – Mavin is a search engine for collectibles, you can get insights into what your collectibles are worth. Similar to pricecharting, they are offering the API for developers. So you can simply get the actual and historical prices for cards, comics or coins.

ShipMyCards – Shipmycards is an interesting project that can become your tax-efficient storage facility with your own USA shipping address. The main business is in the cards but they also support vinyl records, magazines, comic books, memorabilia, or even shoes. In general, you will get your own US address where you send your orders from eBay or other marketplaces. They will help you with collecting, grading, insurance, and final shipment. Great for people outside North America.

For Magic the Gathering and Other TCGs

Card Conduit – Have you found your collection of Magic cards from your teenage times and do you want to sell them? The card conduit is a really smart way how to sell Magic The Gathering in a very transparent and easy way. You simply send your cards via postage and they will price them and sell them for you. You exactly know how much you get for each of the cards because they can automatically identify them and get the best price for your cards. This is a very nice tool with amazing support.

META TCG is a project similar to Card Conduit but focused on Pokémon, Magic The Gathering and Yu-Gi-Oh! You just send your bulk submission via the post office and you get your payments via PayPal.

To Keep Up With News & Stats

CardLines – CardLines is a website where collectors can get information and read news related to sports cards, trading cards and other collectibles. The articles are released daily and if you are an active collector, this one is great to read. The site is trying to monitor the latest releases and there are a lot of tips for collectors. It also has its own small e-shop where you can buy some hobby boxes.

Universal Pop Report by Gemrate – is an amazing site for getting population reports and statistics of cards. The best thing is the grading stats for major grading companies – with this, you will know how many of the cards were graded by PSA or Beckett. In their blog, you can find the grading recap where you can find monthly statistics.

Sports Cards Calendar – This is a great way how to stay updated on upcoming sports cards. On the cardboard connection website, you can find checklists for almost all the sets.

Visual AI Infrastructure for Collectibles by Ximilar

Lastly, I would like to list the solutions we’ve been building for businesses such as collector marketplaces, comparison websites, card dealers, and their mobile applications. We are a SaaS company, focusing on AI, computer vision and visual data, so our tools can be used online via REST API.

Simply said, when it comes to AI for collectibles, we get quite enthusiastic. Currently, we provide:

• The most accurate sports card recognition API in the world – we can recognize parallels and holographic variants with great accuracy, and our growing database already contains millions of cards
• The most accurate trading card identification API in the world. For instance, we are achieving 99 % accuracy for Magic The Gathering, Pokemon and Yu-Gi-Oh!
• An online card grading and card condition system for smartphone cameras and scanners, also available via API
• Visual search and similarity systems for collectibles, which enable your customers to search your catalog of collectibles via pictures
• Product description systems (described here on fashion use case) – for automatization of submission of products on your marketplace
• Image upscaling system – the resolution of your images can be enhanced up to 8x easily
• Grading slab reading with OCR, which extracts the content of the slab, such as grade, the certificate number, year, and player and card name
• Background removal system – in case you need to exactly cut out the card

Our systems are built to analyze large datasets with speed & accuracy. They’re ready to use right away and customizable for specific image collections.

We are continuously improving the models, extending our sports card database and enhancing the speed of the recognition process. We are improving the parallels/refractors identification of sports cards, and our TCG identifier can manage language variations (US, Japanese, Chinese, Korean, …) and different editions (1st edition of Pokemons, MTGs editions). If you would like to help with an API integration, we are here to help. Just reach out via our chat or contact form.

The post The Best Online Tools, Apps, and Services for Card 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.

Today we are releasing three public API endpoints for evaluating card grade, centering and card condition with AI:

• Card Grading – the most complex endpoint that evaluates corners, edges, surface and centering
• Card Centering – computing just the centering of the card
• Card Condition – simple API for getting condition of the card for marketplace (ebay) submission

In this blog post, I would like to get more in-depth about the AI card grading solution. How we built it, what are the pros and cons, how it is different from PSA grading or Beckett grading services, and how you can use it via REST API for your website or app.

AI Card Grading Services as API

With the latest advances in artificial intelligence, it is becoming increasingly common in our daily lives, and collectible cards are a field that doesn’t get left behind. A lot of startups are developing their own card grading, identification, scanning and documenting systems. Some of them were already successfully sold to big players like eBay or PSA. Just to mention a few:

• CollX raises $5.5M to scan and evaluate the value of trading cards

• eBay acquires several startups from the collectors industry like TCGplayer and 3PM Shield LLC

• Collectible card trading platform Alt raises $75M

• PSA Acquires Genamint to introduce Next-Generation Grading process

• AI trading card startup Ludex raises $8M

To understand why card grading is so popular, let’s look at the standard grading process and how the industry works.

Standard Grading Process

Card grading has gained widespread popularity in the world of collectibles by offering a trusted way to assess trading cards to collectors. It’s a method that gives a fair and unbiased evaluation of a card’s condition, ensuring its authenticity and value. This appeals to both seasoned collectors who want to preserve their cards’ worth and newcomers looking to navigate the collectible market confidently.

The process involves sending cards to experts who carefully inspect them for qualities like centering, corners, edges, and surface. The standard grading process for trading cards involves these key steps:

1. Submission: Collectors send their cards to grading companies.

2. Authentication: Cards are checked for authenticity.

3. Grading: Cards are assessed for condition and assigned a grade from 1 to 10 on a grading scale by an expert.

4. Encapsulation: Graded cards are sealed in protective holders.

5. Labelling & Certification: Labels with card details and grades are added. Cards’ information is recorded for verification. Special labels (such as fugitive ink, QR codes, or serial numbers) are introduced to prevent tampering.

6. Return/Sale: Graded cards are returned to owners or sold for higher value.

Costs of Grading Services

The price for submitting cards and their grading depends on the company and the card. For example, the minimal grading price per card by PSA (Professional Sports Authenticator) is 15 USD, and it’s much more for more expensive cards.

You can pay hundreds of dollars if you have some rare baseball card from Topps or non-sports cards from Magic The Gathering or Yu-Gi-Oh! If your modern card collection contains hundreds of cards, the pricing can reach astronomical values. Of course, grading often makes the card’s value higher, depending on its condition and grade.

Pros And Cons of Classic Grading

Besides its costliness, classic grading has several other drawbacks:

• It is a time-consuming offline process that is not particularly ideal for large-scale grading of whole collections.
• Some grading companies would only grade cards with minimum submission value (declared value that is used for insurance).
• Also, customers can usually submit only cards from popular series such as Pokémon, Magic The Gathering, Yu-Gi-Oh!, Sport Topps cards, and Sport Panini cards.

Of course, there are also advantages – like a physically sealed slab with a graded card, confirming its authenticity, and grading done by experts who can look at a card from all different angles and not just from a single image.

Nevertheless, there are a lot of steps involved in card grading, and the entire process takes a lot of time and effort. AI grading can help with the entire workflow, from authentication to grading and labelling.

Computer vision can easily and consistently spot printing defects, analyze corners and edges individually and compute centering in a matter of seconds and for a fraction of the price.

Introducing Online AI Card Grading REST API Service

Fast & Affordable AI Card Grading

Our intention is by no means to replace expert grading companies like PSA, BGS, SGC or CGC with AI-powered card grading. We would rather like it to be a faster, more consistent & cheaper alternative for anyone who needs bulk pre-grading of their collections.

One use case for our AI grading service is to use it to automate the estimation of the declared value of the card. A declared value is the estimated value of the collectible card after PSA has graded it (read PSA’s explanation here).

First, you will submit your card for grading by just sending the photo to our API. After obtaining a grade from our service, you can use our visual search system or card ID for a price guide. Actually, you will not only get the final grade of the card but a detailed grading breakdown (for edges, corners, centering, and surface). Then you can decide by yourself if you want to spend more money for physical grading or to sell it on eBay.

How Do We Train AI to Grade Cards?

To build an AI grading system powered by computer vision and machine learning techniques, we needed a lot of data that imitated real-world use cases (usually user-generated content such as smartphone pictures).

We spent a lot of time building our own dataset, including damaging our own cards. Our purpose from the beginning was to have a grader that would work both on sports cards and trading card games (TCGs), as well as images of different qualities and with different positioning of the cards.

AI Card Grader Consists of Several AI Models

Our card grading solution integrates a number of machine learning models trained on specific datasets. After you upload a photo of a card, the system needs to be able to correctly detect its position. It then identifies the type of the card: a sports card or a trading card game. Another recognition model identifies whether the picture shows the front or back of the card.

After localization & simple identification, the card gets an individual evaluation of its parts. We trained numerous models for individual grading of corners, edges, card surface, and centering, in accordance with grading standards such as PSA or Beckett.

Of course, different types of cards require a different approach, which is why, for example, we have two different models for corners. While sports cards should have sharp corners, TCG cards are typically more rounded.

From the individual grades, we compute a final grade with condition evaluation. Another model is identifying autographed cards. The cards with autographs are generally more valuable.

The big advantage is that the output of the card grading is easy to visualize. That is why we also provide a simple image with the report for each graded card. There you can see a detailed grading breakdown for every part of the card.

Limitations of AI and Machine Learning in Card Grading

Of course, both humans and AI can make mistakes. There are some limitations of the system. Estimating card grades from the images requires relatively high-resolution images, with good lighting conditions and with low post-processing.

As a matter of fact, a lot of modern cameras in smartphones are currently not very good at close-up photos. Their sensors have gotten bigger over the years, and their AI is upscaling the photos. This makes them artificially sharp with cartoon-like effects. This can of course corrupt the overall results. However, as I previously mentioned, that is why we train the models on real-life images and gradually improve their performance.

Let’s Get Some Cards Graded Via Our Online API

Modern Basketball Card

We can test our AI grader via Ximilar App. For this purpose, I chose one of the classic basketball cards of Michael Jordan. BGS (Beckett) gave this card a grade of 6 (EX-MT).

Our online grading system assigned this card a final grade of 6.5. The centering is quite off, so the system graded it 6/10. The grading is still not perfect, as it misses the surface by quite a large margin. However, the final grade is quite close to the one received by Beckett.

In the breakdown image, you can see how the system evaluated individual parts of the card. The lines are drawn on the image, so you can see the details of individual grades for corners and edges. We hope that this brings more transparency to the algorithmic grading.

Vintage Baseball Card

Now let’s take a look at an image of a vintage sports card without an autograph. As an example, I chose the baseball card with Ed Mathews.

The final grade that the card receives is 6.0. The average corner value assigned by the system is 4.0 and edges are 7.0. The grade for the surface is 5.5 and the centering is 7.0 (left/right is 36/65 and top/bottom is 38/62).

We can take a look at the corners and think whether a professional grader would assign the same values. I personally think that the grade is reasonable. However, getting grades from a single image is hard. We’re also not trying to make the values precise up to decimals (e.g., 4.12453 for the upper left corner). We want this to be an affordable soft pre-grading solution.

Card corners are a bit blurry, so ideally, we would like to have a sharper image. However, we can see that the corners are not in the range of 7–10 grades but rather lower (4-6).

How Do We Compute the Final Grade?

We compute the final grade for corners and edges simply as an average of the individual values. We trained the centering grader according to the Beckett grading scale. It is in our opinion much better (has higher demands) than PSA in this case. So to get 10 points for centering, you need to have a 50/50 ratio – on top/bottom and left/right.

The good thing is, that since we provide values for all parts of the card, you don’t need to use our final grades. You can actually create and use your own formula for computing the final grade.

Card Centering API with AI

Some of our customers would like to compute just the centering of the card. That is why we publish also endpoint for this. It will return you offsets from left, right and top and bottom borders of the card. The offsets are relative and also absolute so you can visualize it in your application. Each API response contains image with visualized centering as part of the output:

Lightweight Grading, alias Card Condition Assessment

For customers that want to submit cards to online marketplaces and need to know just the condition of the card like Near Mint, Lightly Played, Heavily Played or Damaged we offer an additional endpoint for getting rough condition of your card. Because this endpoint (/v2/condition) is much simpler and also significantly cheaper than our /v2/grade endpoint. It’s great for a massive amount of data and suitable for collector shops all over the world. The API endpoint can be called from your application or we can write your own script that is able to analyze images/cards from Fujitsu scanners (Fujitsu FI-8170). If you also want to have a card identification service, our visual search AI can identify the TCGs like Pokemon, Magic The Gathering or Yugioh! with more than 98% accuracy.

You can ask to return the condition in several different formats like TCGPlayer, Ebay or our own.

The more about /v2/condition endpoint can be found in our documentation.

How You Can Test Ximilar Card Grader?

To test our online card grader API, you will need to log into the Ximilar App, where it is currently available to users of all plans for testing purposes. We are also currently working on a public demo.

The system is not perfect, neither is the real human grader. It will take us some time to develop something that will be near perfect and very stable. But I believe that we are on the right track to make AI-powered solutions in the collectibles industry more accessible and cheaper.

To Sum Up

The AI card grader is just one of many solutions by Ximilar that the collector community can use. Make sure to check out our AI Recognition of Collectibles. It is a universal service for the automated detection and recognition of all kinds of collectible items.

Automatic Recognition of Collectibles

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

Read more

If you would like us to customize any solution for collectors, just contact us and we will get back to you. We created these solutions (Card Identification and Card Grading) to be the best publicly available AI tools for collectors.

The post AI Card Grading – Automate Sports Cards Pre-Grading appeared first on Ximilar: Visual AI for Business.

That is why we in Ximilar have recently developed an OCR system, integrated it with ChatGPT and made it available via API. It is one of the first publicly available services combining OCR software and the GPT model, supporting several alphabets and languages. In this article, I will provide an overview of what OCR and ChatGPT are, how they work, and – more importantly – how anyone can benefit from their combination.

What is Optical Character Recognition (OCR)?

OCR (Optical Character Recognition) is a technology that can quickly scan documents or images and extract text data from them. OCR engines are powered by artificial intelligence & machine learning. They use object detection, pattern recognition and feature extraction.

An OCR software can actually read not only printed but also handwritten text in an image or a document and provide you with extracted text information in a file format of your choosing.

How Optical Character Recognition Works?

When an OCR engine is provided with an image, it first detects the position of the text. Then, it uses AI model for reading individual characters to find out what the text in the scanned document says (text recognition).

This way, OCR tools can provide accurate information from virtually any kind of image file or document type. To name a few examples: PDF files containing camera images, scanned documents (e.g., legal documents), old printed documents such as historical newspapers, or even license plates.

Most OCR tools are optimized for specific languages and alphabets. We can tune these tools in many ways. For example, to automate the reading of invoices, receipts, or contracts. They can also specialize in handwritten or printed paper documents.

The basic outputs from OCR tools are usually the extracted texts and their locations in the image. The data extracted with these tools can then serve various purposes, depending on your needs. From uploading the extracted text to simple Word documents to turning the recognized text to speech format for visually impaired users.

OCR programs can also do a layout analysis for transforming text into a table. Or they can integrate natural language processing (NLP) for further text analysis and extraction of named entities (NER). For example, identifying numbers, famous people or locations in the text, like ‘Albert Einstein’ or ‘Eiffel Tower’.

Technologies Related to OCR

You can also meet the term optical word recognition (OWR). This technology is not as widely used as the optical character recognition software. It involves the recognition and extraction of individual words or groups of words from an image.

There is also optical mark recognition (OMR). This technology can detect and interpret marks made on paper or other media. It can work together with OCR technology, for instance, to process and grade tests or surveys.

And last but not least, there is intelligent character recognition (ICR). It is a specific OCR optimised for the extraction of handwritten text from an image. All these advanced methods share some underlying principles.

What are GPT and ChatGPT?

Generative pre-trained transformer (GPT), is an AI text model that is able to generate textual outputs based on input (prompt). GPT models are large language models (LLMs) powered by deep learning and relying on neural networks. They are incredibly powerful tools and can do content creation (e.g., writing paragraphs of blog posts), proofreading and error fixing, explaining concepts & ideas, and much more.

The Impact of ChatGPT

ChatGPT introduced by OpenAI and Microsoft is an extension of the GPT model, which is further optimized for conversations. It has had a great impact on how we search, work with and process data.

GPT models are trained on huge amounts of textual data. So they have better knowledge than an average human being about many topics. In my case, ChatGPT has definitely better English writing & grammar skills than me. Here’s an example of ChatGPT explaining quantum computing:

It is no overstatement to say that the introduction of ChatGPT revolutionized data processing, analysis, search, and retrieval.

How Can OCR & GPT Be Combined For Smart Text Extraction

The combination of OCR with GPT models enables us to use this technology to its full potential. GPT can understand, analyze and edit textual inputs. That is why it is ideal for post-processing of the raw text data extracted from images with OCR technology. You can give the text to the GPT and ask simple questions such as “What are the items on the invoice and what is the invoice price?” and get an answer with the exact structure you need.

This was a very hard problem just a year ago, and a lot of companies were trying to build intelligent document-reading systems, investing millions of dollars in them. The large language models are really game changers and major time savers. It is great that they can be combined with other tools such as OCR and integrated into visual AI systems.

It can help us with many things, including extraction of essential information from images and putting them into text documents or JSON. And in the future, it can revolutionize search engines, and streamline automated text translation or entire workflows of document processing and archiving.

Examples of OCR Software & ChatGPT Working Together

So, now that we can combine computer vision and advanced natural language processing, let’s take a look at how we can use this technology to our advantage.

Reading, Processing and Mining Invoices From PDFs

One of the typical examples of OCR software is reading the data from invoices, receipts, or contracts from image-only PDFs (or other documents). Imagine a part of invoices and receipts your accounting department accepts are physical printed documents. You could scan the document, and instead of opening it in Adobe Acrobat and doing manual data entry (which is still a standard procedure in many accounting departments today), you would let the automated OCR system handle the rest.

Scanned documents can be automatically sent to the API from both computers and mobile phones. The visual AI needs only a few hundred milliseconds to process an image. Then you will get textual data with the desired structure in JSON or another format. You can easily integrate such technology into accounting systems and internal infrastructures to streamline invoice processing, payments or SKU numbers monitoring.

Trading Card Identifying & Reading Powered by AI

In recent years, the collector community for trading cards has grown significantly. This has been accompanied by the emergence of specialized collector websites, comparison platforms, and community forums. And with the increasing number of both cards and their collectors, there has been a parallel demand for automating the recognition and cataloguing collectibles from images.

Ximilar has been developing AI-powered solutions for some of the biggest collector websites on the market. And adding an OCR system was an ideal solution for data extraction from both cards and their graded slabs.

Automatic Recognition of Collectibles

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

Read more

We developed an OCR system that extracts all text characters from both the card and its slab in the image. Then GPT processes these texts and provides structured information. For instance, the name of the player, the card, its grade and name of grading company, or labels from PSA.

Needless to say, we are pretty big fans of collectible cards ourselves. So we’ve been enjoying working on AI not only for sports cards but also for trading card games. We recently developed several solutions tuned specifically for the most popular trading card games such as Pokémon, Magic the Gathering or YuGiOh! and have been adding new features and games constantly. Do you like the idea of trading card recognition automation? See how it works in our public demo.

How Can I Use the OCR & GPT API On My Images or PDFs?

Our OCR software is publicly available via an online REST API. This is how you can use it:

1. Log into Ximilar App

   • Get your free API TOKEN to connect to API – Once you sign up to Ximilar App, you will get a free API token, which allows your authentication. The API documentation is here to help you with the basic setup. You can connect it with any programming language and any platform like iOS or Android. We provide a simple Python SDK for calling the API.

   • You can also try the service directly in the App under Computer Vision Platform.

2. For simple text extraction from your image, call the endpoint read.

   https://api.ximilar.com/ocr/v2/read

3. For text extraction from an image and its post-processing with GPT, use the endpoint read_gpt. To get the results in a deserved structure, you will need to specify the prompt query along with your input images in the API request, and the system will return the results immediately.

   https://api.ximilar.com/ocr/v2/read_gpt

4. The output is JSON with an ‘_ocr’ field. This dictionary contains texts that represent a list of polygons that encapsulate detected words and sentences in images. The full_text field contains all strings concatenated together. The API is returning also the language name (“lang_name”) and language code (“lang”; ISO 639-1). Here is an example:

   {
     "_url": "__URL_PATH_TO_IMAGE__
     "_ocr": {
        "texts": [
          {
             "polygon": [[53.0,76.0],[116.0,76.0],[116.0,94.0],[53.0,94.0]],
             "text": "MICKEY MANTLE",
             "prob": 0.9978849291801453
          },
          ...
        ],
        "full_text": "MICKEY MANTLE 1st Base Yankees",
        "lang_name": "english",
        "lang_code": "en
     }
   }

   Our OCR engine supports several alphabets (Latin, Chinese, Korean, Japanese and Cyrillic) and languages (English, German, Chinese, …).

Integrate the Combination of OCR and ChatGPT In Your System

All our solutions, including the combination of OCR & GPT, are available via API. Therefore, they can be easily integrated into your system, website, app, or infrastructure.

Here are some examples of up-to-date solutions that can easily be built on our platform and automate your workflows:

• Detection, recognition & text extraction system – You can let the users of your website or app upload images of collectibles and get relevant information about them immediately. Once they take an image of the item, our system detects its position (and can mark it with a bounding box). Then, it recognizes their features (e.g., name of the card, collectible coin or comic book), extracts texts with OCR and you will get text data for your website (e.g., in a table format).

• Card grade reading system – If your users upload images of graded cards or other collectibles, our system can detect everything including the grades and labels on the slabs in a matter of milliseconds.

• Comic book recognition & search engine – You can extract all texts from each image of a comic book and automatically match it to your database for cataloguing.

• Giving your collection or database of collectibles order – Imagine you have a website featuring a rich collection of collectible items, getting images from various sources and comparing their prices. The metadata can be quite inconsistent amongst source websites, or be absent in the case of user-generated content. AI can recognize, match, find and extract information from images based purely on computer vision and independent of any kind of metadata.

Let’s Build Your Solution

If you would like to learn more about how you can automate the workflows in your company, I recommend browsing our page All Solutions, where we briefly explained each solution. You can also check out pages such as Visual AI for Collectibles, or contact us right away to discuss your unique use case. If you’d like to learn more about how we work on customer projects step by step, go to How it Works.

Ximilar’s computer vision platform enables you to develop AI-powered systems for image recognition, visual quality control, and more without knowledge of coding or machine learning. You can combine them as you wish and upgrade any of them anytime.

Don’t forget to visit the free public demo to see how the basic services work. Your custom solution can be assembled from many individual services. This modular structure enables us to upgrade or change any piece anytime, while you save your money and time.

The post When OCR Meets ChatGPT AI in One API appeared first on Ximilar: Visual AI for Business.

Let’s take a look at what image regression is and how it works, including examples of the most common applications. More importantly, I will tell you how you can train your own regression system on a no-code computer vision platform. As more and more customers seek to extract information from pictures, this new feature is sure to provide Ximilar’s customers with the tools they need to stay ahead of the curve in today’s highly competitive AI-driven market.

What is the Difference Between Image Categorization and Regression?

Image recognition models are ideal for the recognition of images or objects in them, their categorization and tagging (labelling). Let’s say you want to recognize different types of car tyres or their patterns. In this case, categorization and tagging models would be suitable for assigning discrete features to images. However, if you want to predict any continuous value from a certain range, such as the level of tyre wear, image regression is the preferred approach.

Image regression is an advanced machine-learning technique that can predict continuous values within a specific range. Whenever you need to rate or evaluate a collection of images, an image regression system can be incredibly useful.

For instance, you can define a range of values, such as 0 to 5, where 0 is the worst and 5 is the best, and train an image regression task to predict the appropriate rating for given products. Such predictive systems are ideal for assigning values to several specific features within images. In this case, the system would provide you with highly accurate insights into the wear and tear of a particular tyre.

How to Train Image Regression With a Computer Vision Platform?

Simply log in to Ximilar App and go to Categorization & Tagging. Upload your training pictures and under Tasks, click on Create a new task and create a Regression task.

You can train regression tasks and test them via the same front end or with API. You can develop an AI prediction task for your photos with just a few clicks, without any coding or any knowledge of machine learning.

This way, you can create an automatic grading system able to analyze an image and provide a numerical output in the defined range.

Use the Same Training Data For All Your Image Classification Tasks

Both image recognition and image regression methods fall under the image classification techniques. That is why the whole process of working with regression is very similar to categorization & tagging models.

Both technologies can work with the same datasets (training images), and inputs of various image sizes and types. In both cases, you can simply upload your data set to the platform, and after creating a task, label the pictures with appropriate continuous values, and then click on the Train button.

Apart from a machine learning platform, we offer a number of AI solutions that are field-tested and ready to use. Check out our public demos to see them in action.

If you would like to build your first image classification system on a no-code machine learning platform, I recommend checking out the article How to Build Your Own Image Recognition API. We defined the basic terms in the article How to Train Custom Image Classifier in 5 Minutes. We also made a basic video tutorial:

Neural Network: The Technology Behind Predicting Range Values on Images

The most simple technique for predicting float values is linear regression. This can be further extended to polynomial regression. These two statistical techniques are working great on tabular input data. However, when it comes to predicting numbers from images, a more advanced approach is required. That’s where neural networks come in. Mathematically said, neural network “f” can be trained to predict value “y” on picture “x”, or “y = f(x)”.

Neural networks can be thought of as approximations of functions that we aim to identify through the optimization on training data. The most commonly used NNs for image-based predictions are Convolutional Neural Networks (CNNs), visual transformers (VisT), or a combination of both. These powerful tools analyze pictures pixel by pixel, and learn relevant features and patterns that are essential for solving the problem at hand.

CNNs are particularly effective in picture analysis tasks. They are able to detect features at different spatial scales and orientations. Meanwhile, VisTs have been gaining popularity due to their ability to learn visual features without being constrained by spatial invariance. When used together, these techniques can provide a comprehensive approach to image-based predictions. We can use them to extract the most relevant information from images.

What Are the Most Common Applications of Value Regression From Images?

Estimating Age From Photos

Probably the most widely known use case of image regression by the public is age prediction. You can come across them on social media platforms and mobile apps, such as Facebook, Instagram, Snapchat, or Face App. They apply deep learning algorithms to predict a user’s age based on their facial features and other details.

Needless to say, these plugins are not always correct and can sometimes produce biased results. Despite this limitation, various image regression models are gaining popularity on various social sites and in apps.

Ximilar already provides a face-detection solution. Models such as age prediction can be easily trained and deployed on our platform and integrated into your system.

Value Prediction and Rating of Real Estate Photos

Pictures play an essential part on real estate sites. When people are looking for a new home or investment, they are navigating through the feed mainly by visual features. With image regression, you are able to predict the state, quality, price, and overall rating of real estate from photos. This can help with both searching and evaluating real estate.

Custom recognition models are also great for the recognition & categorization of the features present in real estate photos. For example, you can determine whether a room is furnished, what type of room it is, and categorize the windows and floors based on their design.

Additionally, a regression can determine the quality or state of floors or walls, as well as rank the overall visual aesthetics of households. You can store all of this information in your database. Your users can then use such data to search for real estate that meets specific criteria.

Determining the Degree of Wear and Tear With AI

Visual AI is increasingly being used to estimate the condition of products in photos. While recognition systems can detect individual tears and surface defects, regression systems can estimate the overall degree of wear and tear of things.

A good example of an industry that has seen significant adoption of such technology is the insurance industry. For example, startups-like Lemonade Inc, or Root use AI when paying the insurance.

With custom image recognition and regression methods, it is now possible to automate the process of insurance claims. For instance, a visual AI system can indicate the seriousness of damage to cars after accidents or assess the wear and tear of various parts such as suspension, tires, or gearboxes. The same goes with other types of insurance, including households, appliances, or even collectible & antique items.

Our platform is commonly utilized to develop recognition and detection systems for visual quality control & defect detection. Read more in the article Visual AI Takes Quality Control to a New Level.

Automatic Grading of Antique & Collectible Items Such as Sports Cards

Apart from car insurance and damage inspection, recognition and regression are great for all types of grading and sorting systems, for instance on price comparators and marketplaces of collectible and antique items. Deep learning is ideal for the automatic visual grading of collector items such as comic books and trading cards.

By leveraging visual AI technology, companies can streamline their processes, reduce manual labor significantly, cut costs, and enhance the accuracy and reliability of their assessments, leading to greater customer satisfaction.

Automatic Recognition of Collectibles

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

Read more

Food Quality Estimation With AI

Biotech, Med Tech, and Industry 4.0 also have a lot of applications for regression models. For example, they can estimate the approximate level of fruit & vegetable ripeness or freshness from a simple camera image.

For instance, this Japanese farmer is using deep learning for cucumber quality checks. Looking for quality control or estimation of size and other parameters of olives, fruits, or meat? You can easily create a system tailored to these use cases without coding on the Ximilar platform.

Build Custom Evaluation & Grading Systems With Ximilar

Ximilar provides a no-code visual AI platform accessible via App & API. You can log in and train your own visual AI without the need to know how to code or have expertise in deep learning techniques. It will take you just a few minutes to build a powerful AI model. Don’t hesitate to test it for free and let us know what you think!

Our developers and annotators are also able to build custom recognition and regression systems from scratch. We can help you with the training of the custom task and then with the deployment in production. Both custom and ready-to-use solutions can be used via API or even deployed offline.

The post Predict Values From Images With Image Regression appeared first on Ximilar: Visual AI for Business.

Other people in our office spent their youth playing Magic The Gathering (with beautiful artworks), or collecting sports cards with their favorite athletes. In my country, basketball cards and ice hockey cards were really popular. Cards are still loved, played, collected, and traded by geeks, collectors, and sports fans across the world! Their market is growing, and so is the need for automation of image processing on websites and apps for collectors. Right now, cards can be seen even as a great investment.

Where can you use visual AI for cards?

Trading card games (トレーディングカード) can consist of tens of thousands of cards. In principle, building a basic image classifier based solely on image recognition leads to low precision and is simply not enough for more complicated problems.

However, we are able to build a complex similarity system that can recognize, categorize, and find similar cards by a picture. Once trained properly, it can deal with enormous databases of images it never encountered before. With this system, you can find all the information, such as the year of release, card title, exact value, card set, or whether it already is in someone’s collection, with just a smartphone image of the card.

Tip: Check out our Computer Vision Platform to learn about how basic image recognition systems work. If you are not sure how to develop your card search system, just contact us and we will help you.

Collectibles are a big business and some cards are really expensive nowadays. Who knows, maybe you have the card of Charizard or Kobe Bryant hidden in your old box in the attic. We can develop a system for you that can automatically analyze the bulk of trading cards sent from your customers or integrate it into your mobile/smartphone app.

Automatic Recognition of Collectibles

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

Read more

What can visual search do for the trading cards market?

In the last year, we have been building a universal system able to train visual models with numerous applications in image search engines. We already offer visual search services for photo search. But, they are optimized mostly for general and fashion images. This system can be tuned to trading cards, coins, furniture & home decor, arts, and real estate, … there are infinite use cases.

In the last decades, we have all witnessed the growth of the TCG community. However, technologies based on artificial intelligence have not yet been used in this market. Plus, even though the first system for scanning trading cards was released by ebay.com, it was not made available for small shops as an API. And since trading card games and visual AI are a perfect match, we are going to change it – with a card image search.

Tip: Check out Visual Product Search to learn more about visual search applications.

Which TCG cards could visual AI help with?

An image search engine is a great approach when the number of classes for the image classification is high (above 1,000+). With TCGs, each card represents a unique class. A convolutional neural network (CNN) trained as a classifier can have poor results when working with a larger number of classes.

Pokémon TCG contains more than 10,000 cards (classes), Magic the Gathering (MTG) over 50.000, and the same goes for basketball or any other sports cards. So basically, we can build a visual search system for both:

• Trading card games (Magic the Gathering, Lord of the Rings, Pokémon, Yu-Gi-Oh!, One Piece, Warhammer, and so on)
• Collectible sports cards (like Ice Hockey, Football, Soccer, Baseball, Basketball, UFC, and more)

A visual search/recognition technology is starting to be used on E-bay when listing trading and sports cards for sale. However, this is only available in the e-bay app on smartphones. The app has a built-in scanning tool for cards and can find the average price with additional info.

Our service for card image search can be integrated into your website or application. And you can simply connect via API through a smartphone, computer, or sorting machine to find exact cards by photo, saving a lot of time and improving the user experience!

Our Pokémon Card Image Search

We’ve been recently training an AI (neural network) model for Pokémon trading cards, Yugioh! and Magic The Gathering. Why these? Pokémon is the most played TCG in the world, the game has simple rules and an enormous fan base. Very popular are also MTG and Yugioh! Some cards are really expensive, but more importantly, they are traded heavily!

With this model, we built a reverse search for finding the exact Pokémon card, MTG and Yugioh! cards, which achieved 94%+ accuracy (i.e. exact image match). And we are still talking about a prototype in beta version that can be improved to almost 100 %. This search system can return you the edition of the card, language, name of the card, year of release and much more.

If you would like to try the system on these three trading card games, then the endpoint for card identification (/v2/tcg_id) from the Collectibles Recognition service is the right choice for you. If you need to tune it on your image collections or have any other games or cards (sports) then just contact us and we can build a similar service for you.

Automatic grading and inspection of cards with AI

A lot of companies are grading sports & trading cards manually. Our visual AI can be trained to detect corner types, scratches, surface wear, light staining, creases, focus, and borders. The Image recognition models are able to identify marks, wrong cut, lopsided centering, print defects and other special attributes.

For example, PSA is a company that has developed its own grading standards for automatic card grading (MINT). With our platform and team, you can automatize the entire workflow of grading with just one photo. We provide several solutions for computing card grades and card condition.

Customized solution for visual search

With the new custom similarity service, we are able to create a custom solution for trading card image search in a matter of weeks. The process for developing it is quite simple:

1. We will schedule a call and talk about your goals. We will agree on how we will obtain the training data that are necessary to train your custom machine-learning model for the search engine.
2. Our machine-learning specialists will assemble a testable image search collection and train a custom machine-learning model for you in a matter of weeks.
3. After meeting all the requirements of PoC, we will deploy the system to production, and you can connect to it via Rest API.

Image Recognition of Collectibles

Machine learning models bring endless possibilities not only to pop culture geeks and collectors, but to all fields and industries. From personalized recommendations in custom fashion search engines to automatic detection of slight differences in surface materials, the visual AI gets better and smarter every day, making routine tasks a matter of milliseconds. That is one of the reasons why it is an unlimited resource of curiosity, challenges, and joy for us, including being geeks – professionally :).

Ximilar is currently releasing on a ready-to-use computer vision service able to recognize collectibles such as TCG cards, coins, banknotes or post stamps, detect their features and categorize them. Let us know if you’d like to implement it on your website!

If you are interested in a customized AI solution for collector items write us an email and we will get back to you as soon as possible. If you would like to identify cards with our Collectibles Recognition service just sign up via app.ximilar.com.

The post Pokémon TCG Search Engine: Use AI to Catch Them All 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.

The technology behind image similarity is robust, reliable & fast. Built on state-of-the-art (SOTA) AI models and vector databases, you can search millions of images/products in milliseconds. It is used by big e-commerce players as well as small startups for showing visual alternatives or finding products with pictures. Some of our customers have hundreds of millions of images in their collections and do more than 100 million requests per month. Let’s dive into building a superfast similarity search service for your web.

What is Image Similarity?

Image Similarity, or image similarity search, is a visual AI service comparing, grouping, and recommending visually similar images. For example, a typical use case is a product recommendation of similar items in e-shops. It can also be used for reverse image search, where the query is an external image and the results are images from the collection. This approach gives way more accurate results than searching by tags, labels and other attributes.

Ximilar is using state-of-the-art deep learning models for all visual search services. We build our own indexing & searching technology that can run both as a service or on your hardware if needed. The collections can be focused either on product photos, fashion, image matching, or generic photos (stock images).

Features of the Image Similarity Service

Here are several features of the Image Similarity service that we think are crucial:

• Simple access through the Ximilar App (creating a collection on click) and connection to REST API
• The scalable search service can handle collections with hundreds of millions of similar items (images, videos, etc.) and hundreds of requests per second with both CRUD operations and searching
• The ultra-fast and reliable engine that is mostly deployed in large e-commerce platforms – the query for finding the most visually similar product is low latency (in milliseconds)
• The service is customizable – the platform enables you to train your own model for visual similarity search
• Advanced filtering that supports JSON meta-data – if you need to restrict the result to a specific field
• Grouping based on similarity – our search technology can group photos of the same product as one item
• Security and privacy of your data – only meta-data and the visual representation of the images are stored, therefore your images are not stored anywhere
• The service is affordable and cost-effective both for startups and enterprises, offering free plan for tests as well as discounts with your growth over time
• We can deploy it on your hardware, independently of our infrastructure, and also offline – custom similarity model and deployment appropriate to your needs
• Our search engine and machine learning models improve constantly – maintaining much higher quality than any other open-source project & we are able to build custom search engines with trained models

Applications Using Visual Similarity

According to this research by Deloitte, merchandising with artificial intelligence is more and more relevant, and recommendation engines play a vital part in it. Here are a few use cases for visual similarity engines:

• E-shops that use product similarity to help customers to browse and find related products (e.g. in fashion & luxury items, home decor & furniture, art, wall art, prints & posters, collectible trading cards, comics, trademarks, etc.)
• Stock photo databases suggesting similar content – getting visual alternatives of photos, designs, product images, and videos
• Finding the exact products – apps like Vivino for finding wine or any kind of product are easy to develop for us
• Visual similarity duplicate finder (also image matching or deduplication), to know which images are already in your database, or which product photos you can merge together
• Reverse image search – finding a product or an image with a picture online
• Finding similar real estate based for example on interior design, furniture, garden, etc.
• Comparing two images for similarity – for example patterns or designs

Recommending products to your customers has several advantages. Firstly, it creates a better user experience and helps your customers find the right products faster. Secondly, it instantly makes the purchase rate on your web higher. This means a win on both sides – satisfied customers and higher revenue for you. Read more about customer experience and product recommendations in our blog post on fashion search.

Step by step: Building Real-Estate Image Search

Creating the Collection

So let’s take a look at how to easily build your own similarity search engine with the Ximilar platform. The first step is to log in to the Ximilar App. If you don’t have an account, then sign up – it’s free and takes just a minute. After that, on the Dashboard, click on the Visual Search tile and then the Image Similarity service. Then go to the Collections in the left menu and click on Create New Collection. It will show a pop-up with different collection types from which you need to select one.

The collection is a space where you upload your images. With this collection, you are performing queries for search. You can choose from Generic Photo Collection, Product Photo Collection, Dominant Colors Similarity, and Image Matching. Clicking on one of the cards will create a collection for your account.

Each of these collection types is suitable for different types of images:

• Use Generic Photos if you work with stock photos
• Pick a Product Photos collection if you are an e-commerce company
• Select Image Matching to find duplicates in your images
• For the fashion sector, we recommend using a specialized service called Fashion Search
• Custom Similarity is suitable if you are working with another type of data (e.g. videos or 3D models). To do this, please schedule a call with us, and we will develop your own model tuned for your data. For instance, we built a photo search system for the Magic the Gathering Trading Cards for one of our customers.

For this example of real estate, I will use a Generic Photo Collection. The advantage of Generic Photo Collection is that it also supports searching images via text input/query. We usually develop custom similarity models for real estate, when the customers need specific and more accurate results. However, for this simple use case, the generic real estate model will be enough.

Format of Image Similarity Dataset

First, we need to prepare a text file with JSON records. Each record represents an image that we want to store/insert into our collection. The key field is "_url" with the image URL. The advantage of the _url is that you can directly see and inspect the results via app.ximilar.com.

You can also optionally send records with base64 data, this is great if your data are stored locally on your computer. Don’t worry, we are not storing the whole images (data or base64) in the collection database, just URLs with all other metadata present in the records.

The JSON records look like this:

{"_id": "1_1", "_url": "_URL_IMAGE_PATH_", "estate_id": "1", "category": "indoor", "subcategory": "kitchen", "tags": []}
{"_id": "1_2", "_url": "_URL_IMAGE_PATH_", "estate_id": "1", "category": "indoor", "subcategory": "kitchen", "tags": []}
...

If you don’t have image URLs, you can use either "_file" or "_base64" fields for the image data (locally stored "_file" data are automatically converted by the Python client to base64). The image similarity engine is indexing every record of the collection by extracting a representation from the image by a neural network model. However, we are not storing the images in our engine. So, only records that contain "_url" will be visualized in the Ximilar App.

You must store unique identifiers of each image in the "_id" field to identify your images in the collection. The value of this field must be a string. The API endpoint for searching is returning this _id values, that is how you get the results for visual search. You can also store additional fields for every JSON record, and then you can use these fields for filtering, grouping, and tuning the similarity function (see below).

Filling the Collection With Your Data

The next step requires a few lines of code. We are going to insert the prepared images into our collection using our python-client library. You can install the library using pip or directly from GitLab. The usage of the client is very straightforward and basically, you can just use the script tools/collections/insert_json_records.py:

python insert_json_records.py --type generic --auth_token __YOUR_TOKEN__ --collection_id __COLLECTION_ID__ --path /path/to/the/file.json

You will find the collection ID and the Authorization token on the “collection page” in the Ximilar App. This script will run for a few minutes, depending on the size of your image dataset.

Result: Finding Visually Similar Pictures

That was pretty easy, right? Now, if you go to the collections page, you will see something like this:

All images from the JSON file were indexed, and now you can inspect the collection in the Ximilar App. Select the Similarity Search in the left menu of the Image Similarity service and test how the similarity works. You can specify the query image either by upload, by URL, or your IDs, or by choosing one of the randomly selected images from the collection.

Even though we have indexed just several hundred images, you can see that the similarity engine works pretty well. The first image is the query image and the next images are the k-nearest to the query image:

Rest API Connection for Image Search

The next step might be to integrate the service into your application via API. You can either directly use the REST API for searching visually similar images or, if you are using Python, we recommend our Python SDK client like this:

# pip install ximilar-client
from ximilar.client import SimilarityPhotosClient
client = SimilarityPhotosClient("_API_TOKEN_", "_COLLECTION_ID_")
# search k nearest items
client.search({"_id": "1"}, k = 3)
# search by external image
client.search({"_url": "_URL_PATH_"})

Advanced Features for Photo Similarity

The search for visually similar images can be combined with filtering on metadata. This metadata can be stored in the JSON, as in our example with the "category" and "subcategory" fields. In the API, the filtering is specified using a MongoDB-like syntax – see the documentation.

For example, let’s say that we want to search for images similar to the image with ID=1_1 that are indoor photos made in a kitchen. We assume that this meta-information is stored in the “category” and “subcategory” fields of every JSON record. The query will look like this:

client.search({"_id": "1_1"}, filter={"category": "Indoor", "subcategory": "Kitchen"})

If we know that we will often filter on some fields, we can specify them in the “Fields to index” option of the collection to make the query processing more efficient.

Often, your data contains several photos of one “object” – a product or, in our example, real estate. Our service can group the search results not by individual photos but by product IDs. You can set this in the advanced options of the collection by specifying the name of the real estate in the Product ID field, and the magic will happen.

Enhancing Image Similarity Engine with Tags

The image similarity is based purely on the visual content of the image. However, you can use your tags (labels, keywords) to enhance the similarity search. In the example, we assume that the data already contains categories, subcategories, and tags. In order to enhance the visual similarity search with tags, you can fill the “tags” field for every record with your tags, and also use method /v2/visualTagsKNN. After that, your search results will be based on a combination of visual similarity and keywords.

If you don’t have categories and tags, you can create your own photo tagger through our Image Recognition service, and enrich your image data automatically before indexing. The possibilities of image recognition models and their combinations are endless, resulting in highly customizable solutions. Read our guide on how to build your own Image Recognition API.

You can build several models:

• One classifier for categorizing indoor/outdoor/floor plan photos
• One classifier for getting room type (Bedroom, Kitchen, Living room, etc.)
• One tagger for outdoor tags like (Pool, Garden, Garage, House view, etc.)

To Sum Up

The real estate photo similarity search is only one use case of visual similarity from many (fashion, e-commerce, art, stock photos, healthcare…). We hope that you will enjoy working with this service, and we are looking forward to seeing your projects based on it. Thanks to our developers Libor and Ludovit, you can use this service through the frontend app.

Visual Similarity service by Ximilar is unique in terms of search quality, speed performance, and all the possibilities of the API. Our engineers are constantly upgrading the quality of the search, so you don’t have to. We are able to build custom solutions suitable for your data. With multiple collections, you can even A/B test the performance on your websites. This can run in our cloud as SaaS or in your warehouse! If you have more questions about pricing, and technical details,  or you would like to run the similarity search engine on your own machines, then contact us.

The post Image Similarity as a Service For Your Web appeared first on Ximilar: Visual AI for Business.