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.

Today, I’d like to introduce a solution specifically designed for physical stores and warehouses to process their physical card collections quickly and efficiently using card scanners like those from Fujitsu. This tutorial is tailored for shop owners who need to handle large volumes of card images rapidly. We’ve developed a simple yet powerful script in Python 3 for card identification, condition assessment or grading. It also identifies comic books and reads slab labels from companies like PSA or Beckett. The script outputs a CSV file that can be easily imported into Google Sheets or Microsoft Excel. With a few modifications, it can also be adapted for use with your Shopify store or other seller tools, such as for eBay submissions. Let’s dive in and see how this tool can streamline your card-processing workflow!

Capabilities of our AI Solution for Sports Cards and TCGs

Trading Card Games

In the previous blog post, I wrote about our REST API for identifying TCGs, sports cards, and comic book covers. The TCG identification service supports more trading card games, including the most popular ones like Pokémon, Yu-Gi-Oh!, Magic: The Gathering, One Piece, and Lorcana. For some games, it can also identify the correct language version of the card or determine if it is a foil/holographic card. Additionally, for certain TCG games, the system provides links or identification numbers to the TCG Player. You can try how it works here.

Sports Cards

For sports cards, we can identify more than 5 million trading cards across six main sports categories: baseball, hockey, football, soccer, MMA, and basketball cards. Our system also supports the identification of parallel and reprint versions, with continuous improvements. Not only does it provide the best match, but it also offers alternative options to choose from.

If the trading cards are in slabs from major grading companies like PSA, Beckett, CGC, TAG, SGC, or ACE, the system can instantly identify graded cards and provide the slab company, grade, and certificate number.

All Under One API

As you can see, the functionality is complex, offering features such as bulk trading card scanning and language support, resulting in highly accurate identification. I believe that Ximilar Collectibles Recognition services are the most accurate solutions available on the market today. It is a true game-changer for card dealers, other collectors, or companies looking to be independent of third parties like CollX, Kronozio, or Card Dealer Pro, which automatically submit your cards to their marketplaces.

With Ximilar, you can handle your trading card scanning independently using our visual search technology and deep learning models. Our solutions are also designed to suit your specific needs through continuous improvements and customization. Whether you purchase, scan, analyze, search, or sell cards in bulk, our API empowers you to manage your collection without the constraints of third-party services.

How to Analyze TCG and Sports Card Scanners With AI

Step 1 – Run The Cards Through The Scanner

Enough talk! Let’s analyze the bulk of your cards. First, you’ll need a folder with images of your cards. For testing, I’ve selected a small MTG and Pokémon card subset. You can put them on your scanner via top loader (link), or individually. Most card collectors use the Fujitsu Ricoh Fi-8170 scanner, which is one of the best scanners available. It can capture both the front and back sides of the cards.

For our purposes, we will only need the front side of the cards. To avoid unnecessary costs, remove the back side images from the folder or configure your scanner to store only the front side of the cards. Some scanners, like Fujitsu, can produce scan files with names such as 19032024-0001.jpg or 19032024-FRONT-0001.jpg. You can specify the naming format for the scan files. See the following video tutorial on how to set up a Fujitsu scanner via PaperStream Capture by MaxWaxPax:

My recommendation is to use similar settings for your Fujitsu scanner as it is in the video by MaxWaxPax and create multiple profiles for sideways and top-bottom trading card scanning. Ideally set up the scanner to produce only images for the front of the cards or distinguish the images with “front” or “back” suffix in the filename. However, if you already have an unstructured collection of card images, you can fully automate the selection of images showing the front sides using our AI Recognition of Collectibles.

Step 2 – Sign Up To Ximilar Platform

Now, you’ll need an account in our App. Simply sign up with your personal or company email to get your unique API token for service authorization. Once you are in the App, copy your API key to the clipboard and save it into some file. To access the service via API, you’ll need to purchase at least a Business plan. Both tasks – getting the API key and purchasing a Business plan – can be completed in the platform’s settings in a matter of minutes.

Step 3 – Installing Python 3

Before running the script, ensure you have Python 3 installed. Some operating systems already include a version of Python, but we require at least Python 3.6. If you’re unsure, follow this tutorial on RealPython (link), which contains installation steps for Windows, macOS, and Linux:

You should be able to write in your command line, shell or terminal the similar command. Here’s mine at Mac:

michallukac@Michals-MacBook ~ % python --version && pip --version

If you don’t know how to run commands, read a short tutorial on using the terminal/shell/command line. I recommend this tutorial by DjangoGirls or watching some YouTube videos (here’s one for Windows and one for macOS). The output from the command should look similar to my example:

Python 3.9.18

pip 23.1 from /Users/michallukac/env/devel/lib/python3.9/site-packages/pip (python 3.9)

Next, you will need to install Python libraries argparse and requests via pip command:

pip install --upgrade argparse

pip install --upgrade requests

If everything passes, you’re now ready to use the script we’ve prepared to process your folder of card images!

Step 4 – Running The Script On Trading Card Games

Running the script is simple. You’ll need to use a terminal (macOS), shell (Linux), or command line (Windows), which is why we installed Python 3. Download the following file from one of these addresses:

• https://gitlab.com/ximilar-public/ximilar-client/-/blob/master/tools/process_card_scans.py?ref_type=heads

• https://gitlab.com/ximilar-public/ximilar-client/-/raw/master/tools/process_card_scans.py?ref_type=heads&inline=false

Put this file/script next to the folder (tcgscans) with your trading card images or scans and in the terminal, write the following command:

python process_card_scans.py --folder tcgscans --api_key YOURAPIKEY --collectible tcg --output results.csv --select_images all

Hitting the enter will execute the script on the folder of tcgscans, and the progress bar will be shown. The folder will analyze all the images in the folder (select_images). You can interrupt the script (it automatically stores the results every 10 images to your specified output CSV file):

Each analysis of a scan (sports card) will consume 10 credits from your credit supply in your Ximilar account. Our App lets you watch your credit consumption closely under Reports. The Business 100k Plan allows you to analyze 10,000 raw cards. If you need to analyze millions of cards per month or your entire collection at once, reach out to us, and we can offer you a bulk discount.

Step 5 – Analyzing the CSV file

Now we have our CSV file named results.csv. The CSV file contains the following fields: filename (name of the photo in the folder), status (ok or error), side (front or back), subcategory, full_name, name, year, card_number, series, set, set_code, and other additional fields.

The output format of the CSV depends on whether you analyze sports cards, TCG cards, comics, or slabs. Here is a visualization of the CSV file in Visual Studio Code:

We can import the file into Google Sheets or Microsoft Excel spreadsheet, edit it as needed, or generate printable checklists. The columns and data from the CSV can also be easily added to your Shopify product files or used for eBay submissions.

Additional information for card condition (or grading) can be added to the script via the –condition (–grading) parameter. For example, if your sports card scanner provides images with filenames such as 0001.jpg, 0002.jpg, 0003.jpg, etc., the following command will process images with odd numbering (e.g., 0001.jpg, 0003.jpg, …), identify the cards (name, card number, etc.), and also compute their condition (very good, excellent, etc.):

python process_card_scans.py --folder sportsfolder --api_key YOUR_API_KEY --collectible sport --output sport.csv --select_images odd --alternative --condition

Conclusion

With Ximilar’s AI-powered solutions, identifying and documenting your trading cards has never been easier. From trading card scanning, analyzing and organizing, to finding the current average market price, every step is streamlined to save you time and effort. I hope this guide helps you optimize your trading card workflow, making it easier to manage and showcase your collection. Happy collecting, whether it’s baseball or Pokémon cards!

The post How To Scan And Identify Your Trading Cards With Ximilar 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.

Sports Cards Collecting on The Rise

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

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

Our Existing Solutions for Card Collector Sites & Apps

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

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

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

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

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

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

Test it Now in Live Demo

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

The Main Features of Sports Cards

There are several factors determining the value of the card:

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

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

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

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

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

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

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

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

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

• Futera – Futera focuses mostly on soccer cards.

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

Identifying Sports Cards With Artificial Intelligence

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

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

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

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

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

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

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

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

Example: Panini Prizm Football Cards

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

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

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

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

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

How to Identify Sports Cards Via API?

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

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

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

The API response will be as follows:

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

How AI Sports Cards Identification Works?

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

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

What Sports Cards Can Ximilar Identify?

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

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

Who Will Benefit From AI-Powered Sports Cards Identifier?

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

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

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

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

Is My Data Safe?

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

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

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

Sports Cards Recognition Apps You Can Build With Our API

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

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

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

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

AI for Trading Cards and Collectors

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

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

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

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

All Important Things at Hand

Ximilar provides a platform for visual AI, where anyone can create, train and deploy custom-made visual AI solutions based on the techniques of machine learning and computer vision. The platform is accessible via API and a web-based App, where users from all around the world work with both ready-to-use and custom solutions. They implement them into their own apps, quality control or monitoring systems in factories, healthcare tools and so on.

We created the new interface to adapt to the ever-increasing number of services we provide. It now makes better use of both the dashboard and sidebar, showcases useful articles and guides, and provides more support. So, let’s take a look at the major new features!

Service Categories & News

We grouped our services based on how they work with data and the degree of possible customization. After you log into the application, you will see the cards of four service groups with short descriptions on the dashboard. Below them, you can see the newest articles from our Blog, where we publish a lot of useful tips on how to create and implement custom visual AI solutions.

The service groups are following:

1. Ready-to-use Image Recognition includes all the services, that you can use straight away without the need for additional training, custom tags and labels. In principle, these services analyze your data (i.e., your image collection) and provide you with information based on image recognition, object detection, analysis of colors & styles etc. Here you will find Fashion Tagging, Home Decor Tagging, Photo Tagging and Dominant Colors.
2. Custom Image Recognition allows you to train custom Categorization & Tagging and Object Detection models. Flows, that enable you to combine the models, are also under this category. To prepare the training data for object detection seamlessly and fast, you can use our own tool Annotate.
3. Visual Search encompasses all services able to identify, analyze and compare visually similar content. Image Similarity can find, compare and recommend visually similar images or products. You can also use Image Matching to identify duplicates or near-duplicates in your collection, or create a fully custom visual search. Fashion Search is a complex service based on visual search and fashion tagging for apparel image collections.
4. Image Tools are online tools based on computer vision and machine learning that will when provided with an image, modify it. You can then either use the result or implement these image tools in your Flows. Here you will find Remove Background and Image Upscaler.

Do you want to learn more about AI and machine learning? Check the list of The Best Resources on Artificial Intelligence and Machine Learning.

Discover Services

Within the service groups, you can now browse all our services, including the ones that are not in your pricing scheme. Every service dashboard features a service overview and links to documentation, useful guides, case studies & video tutorials.

Do you want to know what you pay for when using our App? Check our article on API credit packs or the documentation.

Guides & Help at Hand

The sidebar underwent some major changes. It now displays all service groups and services. At the bottom, you will find the Guides & Help section with all necessary links to the beginner App Overview tutorial, Guides, Documentation & Contacts in case you need help.

How to make the most of a computer vision solution? Our guides are packed with useful tips & tricks, as well as first-hand experience of our machine learning specialists.

Customize the Sidebar With Favorites

Since each use case is highly specific, our users usually use a small group of services or only one service at a time. That is why you can now pin your most-used services as Favorites.

When you first log into the new front end, all of your previously used services will be marked as favourites. You can then choose which of them will stay on top.

What’s next?

This front-end update is just a first step out of many we’ve been working on. We focus on adding some major features to the platform, such as explainability, as well as custom image regression models. The Ximilar platform provides one of the most advanced Visual AI tools with API on the market, and you can test them for free. Nevertheless, the key to the improvement of our services and App are your opinions and user experience. Let us know what you think!

The post Ximilar Introduces a Brand New App appeared first on Ximilar: Visual AI for Business.

AI Adoption by Companies Worldwide

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

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

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

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

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

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

McKinsey & Company

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

What is Visual Quality Control?

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

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

What Are the Main Benefits of Automatic Visual Inspection?

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

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

Where is Visual Quality Control Used?

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

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

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

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

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

Steel defects [Source: Kaggle]

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

The Technology Behind AI Quality Control

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

Neural Networks – Deep Learning

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

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

[Un|Semi|Self] Supervised Learning

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

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

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

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

Typical Types and Sources of Data for Visual Inspection

Common Data Sources

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

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

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

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

Common Inspection Errors

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

Examples of Datasets for Visual Inspection

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

AI Quality Control Use Cases

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

Our typical workflow for a customized solution is the following:

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

Use case: Image recognition & OCR for wood products

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

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

Use case: Spectrogram analysis from car engines

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

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

Use case: Wind Turbin Blade damages from drone footage

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

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

Ximilar Advantages in Visual AI Quality Control

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

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

Are you interested in creating your own visual control system?

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

To say it simply, artificial intelligence vision got to a point, where it is easy not only to recognize objects in a photo, but also detect features of each thing. That creates a new universe of opportunities for real-world application in e-commerce & traditional industries alike. And Ximilar is a computer vision platform that digs deep into some pretty narrow use cases. So while the big solutions might be great in many ways, Ximilar might very well be the agile alternative.

Ximilar offers you a great cloud AI platform for training your custom image recognition models and advanced visual search services.

Ximilar is Not a Big Corporation

And that is a good thing. Because we keep things simple, streamlined, and we have time to listen to each customer’s needs. We also have the ability to implement new custom features in a timely manner. And we do it as fast as we can, widely benefiting both customers and us, freeing our manpower from manual work.

We at Ximilar create, and continuously improve, advanced visual search, image recognition services & image tools for businesses around the World. That happens in few areas:

• Ximilar recognition app – AI cloud platform for training custom image recognition models
• Ximilar annotate – image annotation tool for creating great datasets
• Visual Search – fashion, stock photos, real estate, home decor, cards, … search engines
• Ready-to-use services – fashion tagging, home-decor and collectibles recognition API
• Image tools – upscale your images and remove the background with API
• and in many cases the most challenging custom AI solutions

We are also not an enterprise that requires millions of users of its services to just stay afloat. See for example how many services were killed by Google. No. Rather than growth in quantity, our center of the universe is how precise we get, and how reliable & sustainable results we deliver. And how we can grow strong together with our customers, or we should rather say our partners.

Here is why Ximilar could be a solid alternative for you if you need to iterate quickly and reach reliable results in narrow fields. Or if you simply need someone who takes your idea further and finds an AI solution to deliver value to your business.

1 – We are focused AI Team

We craft our features to perfection, and we test & use them ourselves. We continuously improve our application for everybody to benefit from new findings in AI vision industry. And we also do things that customers ask for, we don’t just sell access to a platform.

2 – We are an independent company

These days, many companies are created to be acquired. They are created to grow no matter the sustainability of such growth. We are different. Our customers like that we would not disappear tomorrow — getting acquired by a giant and then dissolved into some unreachable feature of some huge app suite is not our target.

3 – We innovate faster

We don’t have a large team and therefore decisions are quick. We are a team of remote professionals working in a field that we truly love and would like to explore to the edge of possibilities. It’s a lot of fun to work on our customers’ challenging tasks. And we are happy to customize any feature. The customer’s budget is the only limit.

4 – Save expenses on AI

Our AI solutions are significantly cheaper than the solutions of big AI players. We are able to save you a lot of money on training and deploying your custom models. For example, training and deploying a model on Google Vertex AI can cost you thousands of dollars, without even calling the API. For Vertex AI AutoML models you are paying for training, deploying and calling a model. Similar pricing for features can be applied to Amazon Rekognition and Azure Custom Vision services. With Amazon Rekognition you are also paying for each hour your model is deployed! On the other hand, AI models built via our platform are trained and deployed for free! You are paying just for calling the API. No more hidden costs.

Head-to-Head Comparison

Narrow Field vs. Generic AI

This one is personal. You would see a lot of simple AI applications, like detecting a cat and a dog in a given — well lit & well shot — picture. But in reality, the bread and butter of applied visual AI is narrow field recognition and analysis of large volumes of images, where the customer needs pretty high accuracy on a specific subject. For example, detect a type of screw on a blurry cellphone photo, shot in bad lighting conditions.

Unlike the giants who mostly sell you ready-made solutions that you can hardly bend to meet your needs, Ximilar is in the other end of the spectrum, brainstorming with customers about how to solve the use case that they have. Being their partner in the path to success.

Examples of such narrow use cases are

• Detecting coffee grounds in a cup – for a customer who receives millions of images to their mobile app used to foretell the future for its users. You wouldn’t believe how many users in coffee-drinking countries use such an app.

• Recognition of trading cards from photo – A cool use-case that was a dream of every geek. Not anymore. Simply snap a photo of a sports card or a game like Pokémon, and the app will identify a card and return a price listed on eBay. You can build your own portfolio tracker and much more with Ximilar.
• Give me a quality rating of a photo – this one was brought up by a hotel reservation site and real estate company. They need to detect the best photos of a property, while the photos are often delivered by a re-seller, or a hotel owner and might not be well shot. And we all know that good photos sell better. Ximilar can help even there with upscaling images and improving their quality.

Lower Price for Higher Accuracy

While the examples above might be fun to read, let’s get to real facts, hardcore numbers and actual user feedback. Because that is a requirement for any business to base its thoughts on. Here are some real-life examples of our customer experiences.

• Ximilar Recognition is cheaper and has comparable accuracy as Microsoft Custom Vision, Amazon Rekognition, Google Vertex AI and IBM Watson. At least several of our customers, and users of the Ximilar App, achieve even better accuracy than with the big cloud solutions. Ximilar allows users to control various parameters of training from a simple GUI.

• UX of Ximilar App is extremely easy to use, also reported by our customers, saying: “Ximilar has a shallow learning curve in comparison to others”. Connection to the API and integration to your systems and apps is easy.
• Ximilar has advanced features for tuning of your recognition tasks which no other services provide — flips, rotations, etc.

• Ximilar Product Similarity and Custom Similarity are unique services for finding visually similar alternatives in fashion, home decor and other image collections
• Ximilar is much more flexible as we are willing to improve our service for your needs – e.g. add more tags to our models — according to your requirements and keep it attached to your data exclusively
• We are cheaper — Google AutoML Vision/Vertex AI is significantly much more expensive than our solution
• Ximilar Fashion Tagging is at the top of abilities in fashion object recognition
• Elaborate management of tags & categories for more projects of higher complexity — we are the only system we know of, that enables users to share training data between categorisation and tagging tasks, chaining recognition models into one API…
• Ximilar, unlike the big competition, is able to install the system on-premise, giving you better control over the system, do a lot of flexible customizations

This is just a brief summary of what we see as benefits for you if you use Ximilar as your partner for pioneering the AI world. We see it now as really just the beginning of all the possibilities that might come in the future of automation and machine learning abilities. We have been around for many years now and Ximilar would surely be around for the years to come. Backing you on the way. Enjoying the exploration.

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New Label Types: Categories & Tags

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