These updates aim to improve your experience and streamline plan setup and credit consumption optimization. Here’s a quick rundown of what’s new.

Plan Setup: Simplified Subscription Management

We’ve revamped the subscription page with new features and better functionality. The Plan Setup page now allows you to choose between Free, Business, or Professional plans, customize your monthly credit supply using a slider, and access our new API Credit Consumption Calculator—a handy tool to help you make informed decisions.

The entire checkout process has been streamlined as well, allowing you to adjust your payment method directly before completing your purchase.

Manage Your Payment Methods and Currencies

You can change the default currency for plan setup and payments in the Settings. To update your payment method, simply access the Stripe Portal from your Plan Overview under “More Actions.” If you prefer a different payment method or have any additional questions, feel free to reach out to us!

Credit Calculator: Estimate & Optimise Your Credit Consumption

One of the most exciting additions to the app is the new Credit Calculator, now available directly within the platform. While this tool was previously featured on our Pricing page, it’s now integrated into the app as well, allowing you to not only estimate your credit needs but also preset your subscription plan directly from the calculator.

Once you’ve adjusted your credits based on projected usage, you can proceed straight to checkout, making the entire process of optimizing and purchasing credits smoother and more efficient.

Plan Overview: A Complete View of Your Plans and Credits

The page Plan Overview gives you a comprehensive view of your active subscription, any past plans, and your pre-paid credit packs. Previously, credit information was limited to your dashboard, but now you have detailed insight into your credit usage and plan history.

In the Plan Overview, you can view all your current active subscription plans. If you upgrade or downgrade, multiple plans may temporarily appear, as credits from your previous plan remain available until the end of the billing period.

Reports: Detailed Insights into Credit Usage

Our new Reports page enables you to gain deeper insights into your API credit usage. It provides two types of reports: credit consumption by AI solution (e.g., Card Grading) and by individual operation within a solution (e.g., “grade one card” within the Card Grading solution).

Credit Packs: Flexibility to Buy Extra Credits Anytime

API Credit packs act as a safety net for unexpected system loads. Now available on their dedicated page, you can purchase additional API credit packs as needed. You can also compare pricing against higher subscription plans and choose the most cost-effective option. Both your active and used credit packs will be displayed on the Plan Overview page.

Invoices: All Your Purchases in One Place

This updated page neatly lists all your invoices, including both subscription payments and one-time credit pack purchases, ensuring that all your financial information is in one place.

Greater Control & Flexibility For the Users

These updates are designed to provide you with greater control, transparency, and flexibility as you build and deploy visual AI solutions. All of these features are now accessible in your sidebar. Check them out, and feel free to reach out with any questions!

The post We Introduce Plan Overview & Advanced Plan Setup appeared first on Ximilar: Visual AI for Business.

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.

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.

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.

In this article, we’ll delve into the importance of selecting the right tools for machine learning model serving, and talk about their pros and cons. We’ll explore various deployment options, serving systems like TensorFlow Serving, TorchServe, Triton, Ray Serve, and MLflow, and also the deployment of specific models such as large language models (LLMs). I’ll also provide some thoughts and recommendations for navigating this ever-evolving landscape.

Machine Learning Models Serving Then and Now

When I first began my journey in the world of machine learning, the landscape was constantly shifting. The frameworks being actively developed and used at the time included Caffee, Theano, TensorFlow (Google) and PyTorch (Meta), all vying for their place in the world of AI. As time has passed, the competition has become more and more lopsided, with TensorFlow and PyTorch leading the way. While TensorFlow has remained the more popular choice for production-ready models, PyTorch has been steadily gaining in popularity, particularly within research circles, for its faster, more intuitive prototyping capabilities.

While there are hundreds of libraries available to train and optimize models, the most popular frameworks such as TensorFlow, PyTorch and Scikit-Learn are all based on Python programming language. Python is often chosen due to its simplicity and the vast amount of libraries for data manipulation. However, it is not the fastest language and can present problems with parallel processing, threads and GIL. Additionally, specialized libraries such as spaCy and PyG are available for specific tasks, such as Natural Language Processing (NLP) and Graph Analysis, respectively. The focus was and still partially is on the optimization of models and architectures. On the other hand, there are more and more problems in machine learning models serving in production because of the large-scale adoption of AI.

Nowadays, even more complex models like large language models (LLM, GPT/LAMMA/BARD) and multi-modal models are in fashion which creates a bigger pressure on optimal model deployment, infrastructure environment and storage capacity. Making machine learning model serving and deployment effective and cheap is a big problem. Even companies like Microsoft or NVIDIA are actively working on solutions that will cut the costs of it. So let’s look into some of the best options that we as developers currently have.

The Machine Learning and DevOps Challenges

Being a Machine Learning Engineer, I can say that training a model is just a small part of the whole lifecycle. Data preparation, deployment process and running the model smoothly for numerous customers is a daily challenge and a major part of the job.

Deployment Strategies

In addition to having to allocate GPU/CPU resources and manage inference speed, the company deploying ML models must also consider the deployment strategy for the trained model. You could be deploying the ML model as an API, running it in a container, or using a serverless platform. Each of these options comes with its own set of benefits and drawbacks, so carefully considering the best approach is essential. When we have a trained model, there are several options on how to use it:

• Deploy it as an API endpoint, sending data in the request and getting results immediately in response. This approach is suitable for faster models that are able to process the data in just a few seconds.
• Deploy it as an API endpoint, but return just a promise or asynchronous response from the model. This is great for computational-intensive models that can take minutes or hours of processing. For example, generative models and upscaling models are slow and require this approach.
• Use a system that is able to serve it for you.
• Use the model locally on your data.
• Deploy models on Smartphones or IoT devices with feed from local sensors.

Other Challenges

The complexity of machine learning projects grows with variables such as:

• The number of models – It is common practice to use multiple models. For example, at this moment, there are tens of thousands of different ML models on the Ximilar platform.
• Model versions – You can train each of your models on different training data (part of the dataset) and mark it as a different version. Model versioning is great if you want to A/B test your ML model, tune your model performance, and for continuous model training.
• Format of models – You can potentially train and save your ML models in various formats. For instance, .h5 which is a Keras/TensorFlow format or .pt (PyTorch) or .onnx for ONNX Runtime. Usually, each framework supports only specific formats.
• The number of frameworks – Served ML models could be trained with different frameworks and their versions.
• The number of the nodes (servers) – Models can be hosted on one or multiple servers and the serving system should be able to intelligently load balance the requests on servers so that none of them is throttled.
• Models storage/registry – You need to store the ML models in some database or storage, such as AWS S3 or local storage
• Speed/performance – The loading time of models from the storage can be critical and can cause a slow inference per sample.
• Easy to use – Calling model via Rest API or gRPC requests, single-or-batch inference.
• Hardware specification – ML models can be deployed on Edge devices or PCs with various architectures.
• GPUs vs CPUs and libraries – Some models must be used only on CPUs and some require a GPU card.

Our Approach to the Machine Learning Model Serving

Several systems were developed to tackle these problems. Serving and deploying machine learning models has come a long way since we founded Ximilar in 2016. Back then, no system was capable of effectively serving hundreds of neural networks for inference.

So, we decided to build our own system for machine learning model serving, and today it forms the backbone of our machine-learning platform. As the use of AI becomes more widespread in companies, newer systems such as TensorFlow Serving emerge quickly to meet the increasing demand.

Which Framework Is The Best?

The Battle of Machine Learning Frameworks

Nowadays, each big tech company has their own solution for machine learning model serving and training. To name a few, PyTorch (TorchServe) and AITemplate by META (Facebook), TensorFlow (TFServing) by Google, ONNX runtime by Microsoft, Triton by NVIDIA, Multi-Model-Server by Amazon and many others like BentoML or Ray.

There are also tens of formats that you can save your ML model in, just TensorFlow alone is able to save into .h5, .pb, saved_model or .tflite formats, each of them serving a different purpose. For example, TensorFlow Lite is great for smartphones. It also loads very fast, so the availability of the model is great. However, it supports only limited operations and more modern architectures cannot be converted with it.

You can also try to convert models from PyTorch or TensorFlow to TensorRT and OpenVino formats. The conversion usually works with basic and most-used architectures. The TensorRT is great if you are deploying ML models on Jetson Nano or Xavier. You can achieve a boost in performance on Intel servers via OpenVino conversion or the Neural Magic library.

The ONNX Format

One notable thing is the ONNX format. The ONNX is not a library for training your machine learning models, ONNX is an open format for storing machine learning models. After the model training, for example, in TensorFlow, you can convert it to ONNX format. You are able to run this converted model via ONNX runtime on almost any platform, programming language, CPU architecture and with preferred hardware acceleration. Sometimes serving a model requires a specific version of libraries, which is why you can solve a lot of problems via ONNX.

Exploration is Key

There are a lot of options for ML model training, saving, conversion and deployment. Every library has its pros and cons, some of them are easy to use for training and development. Others, on the other hand, are specialized for specific platforms or for specific fields (computer vision, recommender systems or NLP).

I would recommend you invest some time in exploring all the frameworks and systems, before deciding which framework you would like to lock in. The competition is rough in this field and every company tries to be as innovative as possible to keep up with the others. Even a Chinese company Baidu developed their own solution called PaddlePaddle. At the end of the article, I will give some recommendations on which frameworks and serving systems you should use and when.

The Best Machine Learning Serving Tools

OK, let’s say that you trained your own model or downloaded one that has already been trained. Now you would like to deploy a machine-learning model in production. Here are a few options that you can try.

If you don’t know how to train a machine learning model, you can start with this tutorial by PyTorch.

Deploy ML Models With API

If you have one or a few models, you can build your own system for ML model serving. With Python and libraries such as Flask or Django, there is a straightforward way to develop a simple REST API. When the web service starts, it loads the model in the background and then every incoming request will call the model on the incoming data.

It could get problematic if you want to effectively work with GPU cards, and handle parallel requests. I would recommend packing the system to Docker and then running it in Kubernetes.

With Kubernetes, Docker and smart load-balancing as HAProxy such a system can potentially scale to bigger volumes. Java or Go languages are also good languages to deploy ML models.

Here is a simple tutorial with a sci-kit-learn model as REST API with Flask.

Now let’s have a look at the open-source serving systems that you can use out of the box, usually with a small piece of code or no code at all.

TensorFlow Serving

GitHub | Docs

TensorFlow Serving is a modern serving system for TensorFlow ML models. It’s a part of TensorFlow Extended developed by Google. The recommended way of using the system is via Docker.

Simply run the Docker pull TensorFlow/serving (optionally TensorFlow/serving:latest-gpu if you need GPU support) command. Just run the image via Docker:

docker run -p 8501:8501 
  --mount type=bind,source=/path/to/my_model/,target=/models/my_model 
  -e MODEL_NAME=my_model -t tensorflow/serving

Now that the system is serving your model, you can query with gRPC or REST calls. For more information, read the documentation. TensorFlow Serving works best with the SavedModel format. The model should define its signature_def_map which will define the inputs and outputs of the model. If you would like to dive into the system then my recommendation is videos by the team itself.

In my opinion, TensorFlow serving is great with simple models and just a few versions. The documentation, however, could be simpler. With advanced architectures, you will need to define the custom operations, which is a big disadvantage if you have a lot of models with more modern operations.

TorchServe

GitHub | Docs

TorchServe is a more modern system than TensorFlow Serving. The documentation is clean and supports basically everything that TF Serving does, however, this one is for PyTorch models. Before serving a PyTorch model via TorchServe, you need to convert them to .mar packages. Basically, the .mar package tells the model name, version, architecture and actual weights of the model. Installation and running are also possible via Docker, and it is very similar to TensorFlow Serving.

I personally like the management of the models, you are able to simply register new models by sending API requests, list models and query statistics. I find the TorchServe very simple to use. Both REST API and gRPC are available. If you are working with pure PyTorch models then the TorchServe is recommended way.

Triton

GitHub | Docs

Both of the serving systems mentioned above are tightly bound to the frameworks of the models they are able to serve. That is probably why Triton has a big advantage over them since it can serve both TensorFlow and PyTorch models. It is also able to serve OpenVINO, ONNX and TensorRT formats! That means it supports all the major formats in the machine learning field. Even though NVIDIA developed it, it doesn’t require a GPU card and can run also on CPUs.

To run Triton, simply pull it from the docker repository via the Docker pull nvcr.io/nvidia/tritonserver command. The triton servers are able to load models from a specific directory called model_repository. Each model is defined with configuration, in this configuration, there is a platform setting that defines a model format. For example, “tensorflow_graphdef” or “onnxruntime_onnx“. In this way, Triton knows how to run specific models.

The documentation is not super-easy to read (mostly GitHub README files) because it is in very active development. Otherwise, working with the models is similar to other serving systems, meaning calling models via gRPC or REST.

Ray Serve

GitHub | Docs

Ray is a general-purpose system for scaling machine learning workloads. It primarily focuses on model serving and providing the primitives for you to build your own ML platform on top.

Ray Serve offers a more Pythonic way of creating your own serving system. It is framework-agnostic and anything that can be run via Python can be run also with Ray. Basically, it looks as simple as Flask. You define the simple Python class for your model and decorate it with a route prefix handler. Then you just call the REST API request.

import requests
from starlette.requests import Request
from typing import Dict

from ray import serve

# 1: Define a Ray Serve deployment.
@serve.deployment(route_prefix="/")
class MyModelDeployment:
    def __init__(self, msg: str):
        # Initialize model state: could be very large neural net weights.
        self._msg = msg

    def __call__(self, request: Request) -> Dict:
        return {"result": self._msg}

# 2: Deploy the model.
serve.run(MyModelDeployment.bind(msg="Hello world!"))

# 3: Query the deployment and print the result.
print(requests.get("http://localhost:8000/").json())

If you want to have more control over the system, Ray is a great option. There is a Ray Clusters library which is able to deploy the system on your own Kubernetes Cluster, AWS or GCP with the ability to configure the autoscaling option.

MLflow

MLflow is an open-source platform for the whole ML lifecycle. From training to evaluation, deployment, tracking, model monitoring and central model registry.

MLflow offers a robust API and several language bindings for the whole management of the machine learning model’s lifecycle. There is also a UI for tracking your trained models. MLflow is really a mature package with a whole bundle of components that your team can use.

Other Useful Tools for Machine Learning Model Serving

• Multi-Model-Server is a similar system to the previous ones. Developed by the Amazon AWS team, the system is able to run models trained with MXNet or converted via ONNX.
• BentoML is a project very similar to MLflow. There are many different tools that data scientists can use for training and deployment processes. The UI looks a bit more modern. BentoML is also able to automatically generate Docker images for your models.
• KServe is a simple system for managing and scaling models on your Kubernetes. It solves the deployment, and autoscaling and provides standardized inference protocol across ML frameworks.

Cloud Options of AWS, GCP and Azure

Of course, every big tech player provides cloud platforms to host and serve your machine learning models. Let’s have a quick look at a few examples.

Microsoft is a big supporter of ONNX, so with Azure Machine Learning services, you are able to deploy your models to the cloud via Python or Azure CLI. The process requires an entry script in Python with two methods: init for initialization of your model and run for inference. You can find the entire workflow in Azure development documentation.

The Google Cloud Platform (GCP) has good support for TensorFlow as it is their native framework. However, Docker deployment is available, so other frameworks can be used too. There are multiple ways to achieve the deployment. The classic way will be using the AI Platform prediction tool or Google Cloud Run. There is also a serverless HTTP endpoint/function, which serves your model stored in the Google Cloud Storage bucket. You define your function in Python with the prediction method and loading of the model.

Amazon Web Services (AWS) also contains multiple options for the ML deployment process and serving. The specialized system for machine learning is Amazon Sagemaker.

All the big platforms allow you to create your own virtual server instances. Create your Kubernetes clusters and use any of the systems/frameworks mentioned earlier. Nevertheless, you need to be very careful because it could get really pricey. There are also smaller players on the market such as Banana, Seldon and Comet ML for training, serving & deployment. I personally don’t have experience with them but they are becoming more popular.

Large Language (LLMs) and Multi-Modal Models in Production

With the introduction of GPT by OpenAI a new class of AI models was introduced – the large language models (LLMs). These models are extremely big, trained on massive datasets and deployed on an infrastructure that requires a whole datacenter to run. “Smaller” – usually open source version – models are released but they also require a lot of computational resources and modern servers to run smoothly.

Recently, several serving systems for these models were developed:

• OpenLLM by BentoML is a nice system that supports almost all open-source models like Llama2. You can just pick one of the models and run the following commands to start with the serving and query the results:

openllm start opt
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'Explain to me the difference between "further" and "farther"'

• vLLM project is a Python library that can help you with the deployment of LLM as an API Server. What is great is that it supports OpenAI-Compatible Server, so you can switch from OpenAI paid service easily to open source variant without modifying the code on the client. This project is being developed at UC Berkeley and it is integrating new techniques for fast inferencing of LLMs.

• SkyPilot – is a great option if you want to run the LLMs on cloud providers such as AWS, Google Cloud or Azure. Because running these models is costly, SkyPilot is able to pick the cheapest provider automatically and launch it as an endpoint.

Ximilar AI Platform

Free Login | Docs

Last but not least, you can use our codeless machine-learning platform. Instead of writing a lot of code, training and deploying an ML model by yourself, you can try it in the Ximilar App. Training image classification and object detection can be done both in the browser App or via API. There is every tool that you would need in the ML model development stage, such as training data/image management, labelling tools, evaluation of your models on testing and training datasets, performance metrics, explanation of models on specific images, and so on.

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.

Once your model is trained, it is deployed as a REST API endpoint. It can be connected to a workflow of more machine learning models working together with conditions like if-else statements. The major benefit is you just connect your system to the API and query the results. All the training and serving problems are solved by us. In the end, you will save a lot of costs because you don’t need to own or rent your infrastructure, serving systems or specialized software engineering team on machine learning.

We built a Ximilar Platform so that businesses from e-commerce, healthcare, manufacturing, real estate and other areas could simply develop their own AI models without coding and with a reasonable budget. For example, on the following screen, you can see our task management for the trading cards collector community.

The great thing is that everything is manageable via REST API requests with JSON responses. Here is a simple curl command to query all models in production:

curl --request GET 
  --url https://api.ximilar.com/recognition/v2/task/ 
  --header 'Content-Type: application/json' 
  --header 'authorization: Token APITOKEN'

Deployment of ML Models is Science

There are a lot of systems that try to make deployment and serving easy. The topic of deployment & serving is broad, with many choices for hardware infrastructure, DevOps, programming languages, system development, costs, storage, and scaling. So it is not easy to pick one. If you would like to dig deeper, I would suggest the following content for further reading:

• For the performance test of serving systems, I recommend a post from Biano that includes testing scripts.

• A nice overview of all the deployment systems is also in a video lecture on the Full Stack Deep Learning course.

My Final Tips & Recommendations

Pick a good framework to start with

Doing machine learning for more than 10 years, my advice is to start by picking a good framework for model development. In my opinion, the best choice right now is PyTorch. Using it is easy and it supports a lot of state-of-the-art architectures.

I used to be a fan of TensorFlow for a long time, but over time, its developers were not able to integrate modern approaches. Also, the backward compatibility is often disrupted and the quality of code is getting worse which leads to more and more bugs in the framework.

Save your models in different formats

Second, save your models in different formats. I would also recommend using ONNX and OpenVino here. You never know when you will need it. This happened to me a few times. We needed to upgrade the server and systems (our production environment), but the new versions of libraries stopped supporting the specific format of the model, so we had to switch to a different one.

Pick a serving system suitable to your needs

If you are a small company, then Ray Serve is a good option. Bigger companies, on the other hand, have complex requirements for development and robust infrastructure. In this case, I would recommend picking more complex systems like MLFlow. If you would like to serve the models on the cloud, then look at a multi-model server. The choice is really based on the use case. If you don’t want to bother with all of this then try our Ximilar Platform, which is a solution model optimization, model validation, data storage and model deployment as API.

I will keep this article updated and if there is some new perspective serving system I will be more than happy to mention it here. After all, machine learning is about constant progress, and that is one of the things I like about it the most.

The post The Best Tools for Machine Learning Model Serving 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.

However, as the world of trading card games expands, collectors increasingly require a versatile trading card game identifier. This tool should swiftly recognize various collectible cards, irrespective of your private collection or database. We accepted this challenge and built a Trading Card Game Identifier (Card ID). In this article, I will describe how it works, and how you can use it for your own App or website. We will also take a brief look at other additions to our Collectibles Recognition: OCR & grading system for both TCGs and sports.

Trading Card Games Identifier

What is the Card Identifier?

Card Identifier is an AI-powered tool by Ximilar able to recognize trading game cards in any image format and provide you with their attributes, such as the name, exact set, series, codes, number, or year of release. It also provides attributes such as information on whether the card is holo (foil-treated) or what alphabet or language it uses.

This solution is an extension of our core service AI Recognition of Collectibles (which does the basic image recognition of all collectible items) and expands its functionalities by detailed identification of specific trading games.

Card ID works independently of keywords and metadata. As a matter of fact, you can use it to generate keywords. You can save the output in JSON or use it for searching and filtering items on your webpage.

The attributes of cards, such as their name, date of release and set, are also typically used to find the trading card’s average price on marketplaces such as TCGPlayer or eBay. That is why for some cards, we can provide links to these sites right away.

There are several use-cases that you can use our card identifier, here are few of them:

• you can connect your card scanner like Fujitsu (fi-8170) and create an system for documenting & digitalising your card collectors inventory, save thousands of hours with AI analysis
• you can build a smartphone app that is able to identify a card from photo and get average price on ebay or tcgplayer
• you can create your own marketplace website for card reselling & listing. Our technology will help with card identification of incoming submissions.

Because our solutions are powered by computer vision, you can upload photos of as many cards as you want, with or without sleeves, under different lighting and conditions.

Which Games Can the Trading Card Game Identifier Recognize?

Pokémon TCG

The Pokémon Trading Card Game is one of the most popular trading games. Fans of all generations and nationalities have been playing Pokémon TCG ever since its release in 1998. Our identifier recognizes Pokémon cards in both English & Japanese and provides their attributes.

Magic The Gathering

MGT is a highly popular game. As of 2023, over 100 MTG sets have been released, with their numbers continually rising, making it increasingly challenging to keep pace with all the sets and new cards. Our identifier provides all basic information about the Magic The Gathering card in an uploaded photo, and we keep adding new attributes.

Yu-Gi-Oh!

Yu-Gi-Oh! is an iconic trading card game based on an anime series. Since its 1999 release, Yu-Gi-Oh! has garnered a dedicated community of players and collectors. Recognized as a top-selling TCG in 2009 by Guinness World Records, with over 22 billion cards sold worldwide, the demand for an AI model to assist with card identification is understandable.

From MetaZoo to Lorcana

TCGs such as MetaZoo TCG, Flesh and Blood TCG, One Piece Card Game, or Lorcana TCG are all smaller or more recent games, but they are starting to be more and more popular both in English-speaking and Asian countries.

Independent of card type, this endpoint will also provide information such as:

• Side – front or back of the card.

• Alphabet – such as Latin, Japanese, Korean, Chinese, and more.

• Holo/Foil – whether the card has a holo effect (aluminium foil).

• Autograph – this particular feature is common rather for baseball and other sports cards.

All this information is necessary to value trading cards properly. For instance, a Japanese card can have a different value than an English one, and a holo card can have a higher value than a regular one.

How Identifying Trading Card Games via API Works?

Connect to API

Once you register in Ximilar App, you will automatically get your own unique API token. You will need at least business pricing plan. Then you can access and use our solutions both via App & API:

• In the App, Card ID is a part of the Collectibles Recognition service. So if you upload your images there, the trading cards in them will be automatically recognized and identified.

• The REST API endpoint is simple to use and easy to integrate into your mobile app, website or card-sorting machines. If you’re new to deploying solutions via API, the API documentation is here to help you with the basic setup. You can also find a lot of helpful information in our Help Center.

• For a lot of cards we are able to provide links to TCG Player or Cardmarket so you will know the price of analysed cards immediatelly.

To access the Card Identifier by Ximilar, use the endpoint /v2/tcg_id:

https://api.ximilar.com/collectibles/v2/tcg_id

We are always here to answer your questions through the contact form or live chat and can also do the setup for you.

Implement Trading Card Game Identifier in Your App

Imagine you are building an app or a site catering to Yu-Gi-Oh! fans and collectors. When a visitor uploads a picture of a new card, our AI Recognition of Collectibles instantly detects the card’s position and confirms it as a trading card. Thanks to its object detection & image recognition capabilities, users can upload pictures containing multiple cards.

Subsequently, the Card ID provides the card’s attributes Name, Full Name, Set, Set Code, Card Number, Rarity and Year. This happens independently of your portfolio (collection) or database.

The identification of the record is fast (usually takes a second to process) and the results are provided in JSON. This way, the user can be provided with structured data on their trading card in a matter of seconds.

The identification works for almost all popular TCGs. And the good news is that our AI for card recognition is so powerful that we can extend it to other games. Let us know if you are missing any games.

New Solutions For Sports Cards

Sports Card Text Analysis With OCR & GPT

Because there are millions of sports cards, and it’s very hard to gather data for them, we have recently released another solution for text extraction from sports cards. The system is accessible via following endpoints:

https://api.ximilar.com/collectibles/v2/card_ocr_id
https://api.ximilar.com/collectibles/v2/sport_id

For the first endpoint. This technology is able to read all the texts in the photo with a card via Optical Character Recognition (OCR) and then provide information on the athlete via Large Language model (LLM) – GPT. This model is still in the works, however, it can help you with the automatization and labelling of the cards. If you have your own collection of sport’s cards then we can build you a precise, fast and affordable AI system for sports card identification.

The second endpoint actually uses a limited sports cards database for identification. You can try to play with both of them and choose the solution that works for you. If you have your own database of sports cards we can build a similar system just on your data.

You can read more about this solution in the article When OCR Meets ChatGPT AI in One API.

Read Graded Slab Labels With AI

Sports card grading is gaining popularity not only in the USA but also in Europe and Asia, as collectors recognize the value of their cards. Having rare foiled cards evaluated by esteemed companies like PSA or Beckett may be a good investment.

Online trading has become a prevalent trend, with eBay leading the pack as the go-to marketplace for collectibles. However, searching for the best deal among thousands of results for a specific query, like a “Michael Jordan Graded Card” can be incredibly time-consuming and challenging.

Our endpoint slab_id reads the graded slabs and helps to automate the identification of promising cards:

https://api.ximilar.com/collectibles/v2/slab_id

It will read the slab and return attributes such as grade, name, grade company and certification number. You can use it to automatically find and filter items with certain grades or conditions (8/9/10, near mint, gem mint, and so on).

Pre-Grading of Sports Cards With AI

We also provide an alternative to the slab reader in case the uploaded card doesn’t have a grade yet. It is an AI-powered grader for websites that evaluate & sell sports cards. The system can grade whole cards as well as individual parts like corners, edges, or centering. It is accessible via endpoint grade (precise) or condition (lightweight and fast):

https://api.ximilar.com/card-grader/v2/grade
https://api.ximilar.com/card-grader/v2/condition

Because identifying grades from a single picture cannot fully replace a professional grader, this endpoint serves mainly as a pre-grading solution. As I write this article, it is currently in beta testing. Nonetheless, it has already proven effective in specific scenarios, particularly with high-resolution pictures of sports cards without sleeves or slabs. This feature was highly requested by many of our customers. So we made it accessible to both Business and Professional plan users.

Solving this challenge is no simple task, and it is a long-term project for us. We are working hard both on gathering training data and improving the model architecture. It serves also as a research project, as we encounter a lot of new and not quite standard things and problems. I will write more about this service, technology, and development in a future blog post. So stay tuned!

Automation in Collectibles Industry Makes Sense

Here are a few reasons why I think the trading card industry is growing rapidly, and will use AI-powered automation more in the future:

• According to Business Research Insights, the global collectible card game market was valued at 13 billion USD in 2021. And it is projected to grow by 16% annually.

• In 2022 alone, 10 million sports cards were graded, indicating significant demand for grading services.

• The TCG potential has caught the attention of major corporations including Disney, evident from their release of the Lorcana TCG in 2023.

• Companies like Nintendo earned nearly 43 million USD from card games playing last year.

• Magic The Gathering remains Hasbro’s most profitable business venture.

Get a Solution Tailored to Your Business

All the services mentioned in this article are easy to combine with each other and with the rest of our solutions. One of the most popular solutions in the field of collectibles is a visual search and similar item recommendation. If you are aiming to have your own visual search engine, I suggest reading Pokémon TCG Search Engine: Use AI to Catch Them All and then contacting us.

The collector community’s feedback and thoughts serve as our primary motivation to develop tailor-made solutions for this amazing field. Contact us anytime and we can discuss your goals.

The post Build Your Own Trading Card Game Identifier With Our API 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.

However, a universal visual search solution is not the only thing that customers around the world will require in the future. Especially smaller companies and startups now more often look for custom or customizable visual search solutions for their sites & apps, built in a short time and for a reasonable price. What does creating a visual search engine actually look like? And can a visual search engine be built by anyone?

This article should provide a bit deeper insight into the technology behind visual search engines. I will describe the basic components of a visual search engine, analyze approaches to machine learning models and their training datasets, and share some ideas, training tips, and techniques that we use when creating visual search solutions. Those who do not wish to build a visual search from scratch can skip right to Building a Visual Search Engine on a Machine Learning Platform.

What Exactly Does a Visual Search Engine Mean?

The technology of visual search in general analyses the overall visual appearance of the image or a selected object in an image (typically a product), observing numerous features such as colours and their transitions, edges, patterns, or details. It is powered by AI trained specifically to understand the concept of similarity the way you perceive it.

In a narrow sense, the visual search usually refers to a process, in which a user uploads a photo, which is used as an image search query by a visual search engine. This engine in turn provides the user with either identical or similar items. You can find this technology under terms such as reverse image search, search by image, or simply photo & image search.

However, reverse image search is not the only use of visual search. The technology has numerous applications. It can search for near-duplicates, match duplicates, or recommend more or less similar images. All of these visual search tools can be used together in an all-in-one visual search engine, which helps internet users find, compare, match, and discover visual content.

And if you combine these visual search tools with other computer vision solutions, such as object detection, image recognition, or tagging services, you get a quite complex automated image-processing system. It will be able to identify images and objects in them and apply both keywords & image search queries to provide as relevant search results as possible.

Different computer vision systems can be combined on Ximilar platform via Flows. If you would like to know more, here’s an article about how Flows work.

Typical Visual Search Engines:
Google Lens & Pinterest Lens

Big visual search industry players such as Shutterstock, eBay, Pinterest (Pinterest Lens) or Google Images (Google Lens & Google Images) already implemented visual search engines, as well as other advanced, yet hidden algorithms to satisfy the increasing needs of online shoppers and searchers. It is predicted, that a majority of big companies will implement some form of soft AI in their everyday processes in the next few years.

The Algorithm for Training
Visual Similarity

The Components of a Visual Search Tool

Multimedia search engines are very powerful systems consisting of multiple parts. The first key component is storage (database). It wouldn’t be exactly economical to store the full sample (e.g., .jpg image or .mp4 video) in a database. That is why we do not store any visual data for visual search. Instead, we store just a representation of the image, called a visual hash.

The visual hash (also visual descriptor or embedding) is basically a vector, representing the data extracted from your image by the visual search. Each visual hash should be a unique combination of numbers to represent a single sample (image). These vectors also have some mathematical properties, meaning you can compare them, e.g., with cosine, hamming, or Euclidean distance.

So the basic principle of visual search is: the more similar the images are, the more similar will their vector representations be. Visual search engines such as Google Lens are able to compare incredible volumes of images (i.e., their visual hashes) to find the best match in a hundred milliseconds via smart indexing.

How to Create a Visual Hash?

The visual hashes can be extracted from images by standard algorithms such as PHASH. However, the era of big data gives us a much stronger model for vector representation – a neural network. A simple overview of the image search system built with a neural network can look like this:

This neural network was trained on images from a website selling cosmetics. Here, it extracted the embeddings (vectors), and they were stored in a database. Then, when a customer uploads an image to the visual search engine on the website, the neural network will extract the embedding vector from this image as well, and use it to find the most similar samples.

Of course, you could also store other metadata in the database, and do advanced filtering or add keyword search to the visual search.

Types of Neural Networks

There are several basic architectures of neural networks that are widely used for vector representations. You can encode almost anything with a neural network. The most common for images is a convolutional neural network (CNN).

There are also special architectures to encode words and text. Lately, so-called transformer neural networks are starting to be more popular for computer vision as well as for natural language processing (NLP). Transformers use a lot of new techniques developed in the last few years, such as an attention mechanism. The attention mechanism, as the name suggests, is able to focus only on the “interesting” parts of the image & ignore the unnecessary details.

Training the Similarity Model

There are multiple methods to train models (neural networks) for image search. First, we should know that training of machine learning models is based on your data and loss function (also called objective or optimization function).

Optimization Functions

The loss function usually computes the error between the output of the model and the ground truth (labels) of the data. This feature is used for adjusting the weights of a model. The model can be interpreted as a function and its weights as parameters of this function. Therefore, if the value of the loss function is big, you should adjust the weights of the model.

How it Works

The model is trained iteratively, taking subsamples of the dataset (batches of images) and going over the entire dataset multiple times. We call one such pass of the dataset an epoch. During one batch analysis, the model needs to compute the loss function value and adjust weights according to it. The algorithm for adjusting the weights of the model is called backpropagation. Training is usually finished when the loss function is not improving (minimizing) anymore.

We can divide the methods (based on loss function) depending on the data we have. Imagine that we have a dataset of images, and we know the class (category) of each image. Our optimization function (loss function) can use these classes to compute the error and modify the model.

The advantage of this approach is its simple implementation. It’s practically only a few lines in any modern framework like TensorFlow or PyTorch. However, it has also a big disadvantage: the class-level optimization functions don’t scale well with the number of classes. We could potentially have thousands of classes (e.g., there are thousands of fashion products and each product represents a class). The computation of such a function with thousands of classes/arguments can be slow. There could also be a problem with fitting everything on the GPU card.

Loss Function: A Few Tips

If you work with a lot of labels, I would recommend using a pair-based loss function instead of a class-based one. The pair-based function usually takes two or more samples from the same class (i.e., the same group or category). A model based on a pair-based loss function doesn’t need to output prediction for so many unique classes. Instead, it can process just a subsample of classes (groups) in each step. It doesn’t know exactly whether the image belongs to class 1 or 9999. But it knows that the two images are from the same class.

Images can be labelled manually or by a custom image recognition model. Read more about image recognition systems.

The Distance Between Vectors

The picture below shows the data in the so-called vector space before and after model optimization (training). In the vector space, each image (sample) is represented by its embedding (vector). Our vectors have two dimensions, x and y, so we can visualize them. The objective of model optimization is to learn the vector representation of images. The loss function is forcing the model to predict similar vectors for samples within the same class (group).

By similar vectors, I mean that the Euclidean distance between the two vectors is small. The larger the distance, the more different these images are. After the optimization, the model assigns a new vector to each sample. Ideally, the model should maximize the distance between images with different classes and minimize the distance between images of the same class.

Sometimes we don’t know anything about our data in advance, meaning we do not have any metadata. In such cases, we need to use unsupervised or self-supervised learning, about which I will talk later in this article. Big tech companies do a lot of work with unsupervised learning. Special models are being developed for searching in databases. In research papers, this field is often called deep metric learning.

Supervised & Unsupervised Machine Learning Methods

1) Supervised Learning

As I mentioned, if we know the classes of images, the easiest way to train a neural network for vectors is to optimize it for the classification problem. This is a classic image recognition problem. The loss function is usually cross-entropy loss. In this way, the model is learning to predict predefined classes from input images. For example, to say whether the image contains a dog, a cat or a bird. We can get the vectors by removing the last classification layer of the model and getting the vectors from some intermediate layer of the network.

When it comes to the pair-based loss function, one of the oldest techniques for metric learning is the Siamese network (contrastive learning). The name contains “Siamese” because there are two identical models of the same weights. In the Siamese network, we need to have pairs of images, which we label based on whether they are or aren’t equal (i.e., from the same class or not). Pairs in the batch that are equal are labelled with 1 and unequal pairs with 0.

In the following image, we can see different batch construction methods that depend on our model: Siamese (contrastive) network, Triplet, or N-pair, which I will explain below.

Triplet Neural Network and Online/Offline Mining

In the Triplet method, we construct triplets of items, two of which (anchor and positive) belong to the same category and the third one (negative) to a different category. This can be harder than you might think because picking the “right” samples in the batch is critical. If you pick items that are too easy or too difficult, the network will converge (adjust weights) very slowly or not at all. The triplet loss function contains an important constant called margin. Margin defines what should be the minimum distance between positive and negative samples.

Picking the right samples in deep metric learning is called mining. We can find optimal triplets via either offline or online mining. The difference is, that during offline mining, you are finding the triplets at the beginning of each epoch.

Online & Offline Mining

The disadvantage of offline mining is that computing embeddings for each sample is not very computationally efficient. During the epoch, the model can change rapidly, so embeddings are becoming obsolete. That’s why online mining of triplets is more popular. In online mining, each batch of triplets is created before fitting the model. For more information about mining and batch strategies for triplet training, I would recommend this post.

We can visualize the Triplet model training in the following way. The model is copied three times, but it has the same shared weights. Each model takes one image from the triplet (anchor, positive, negative) and outputs the embedding vector. Then, the triplet loss is computed and weights are adjusted with backpropagation. After the training is done, the model weights are frozen and the output of the embeddings is used in the similarity engine. Because the three models have shared weights (the same), we take only one model that is used for predicting embedding vectors on images.

N-pair Models

The more modern approach is the N-pair model. The advantage of this model is that you don’t mine negative samples, as it is with a triplet network. The batch consists of just positive samples. The negative samples are mitigated through the matrix construction, where all non-diagonal items are negative samples.

You still need to do online mining. For example, you can select a batch with a maximum value of the loss function, or pick pairs that are distant in metric space.

In our experience, the N-pair model is much easier to fit, and the results are also better than with the triplet or Siamese model. You still need to do a lot of experiments and know how to tune other hyperparameters such as learning rate, batch size, or model architecture. However, you don’t need to work with the margin value in the loss function, as it is in triplet or Siamese. The small drawback is that during batch creation, we need to have always only two items per class/product.

Proxy-Based Methods

In the proxy-based methods (Proxy-Anchor, Proxy-NCA, Soft Triple) the model is trying to learn class representatives (proxies) from samples. Imagine that instead of having 10,000 classes of fashion products, we will have just 20 class representatives. The first representative will be used for shoes, the second for dresses, the third for shirts, the fourth for pants and so on.

A big advantage is that we don’t need to work with so many classes and the problems coming with it. The idea is to learn class representatives and instead of slow mining “the right samples” we can use the learned representatives in computing the loss function. This leads to much faster training & convergence of the model. This approach, as always, has some cons and questions like how many representatives should we use, and so on.

MultiSimilarity Loss

Finally, it is worth mentioning MultiSimilarity Loss, introduced in this paper. MultiSimilarity Loss is suitable in cases when you have more than two items per class (images per product). The authors of the paper are using 5 samples per class in a batch. MultiSimilarity can bring closer items within the same class and push the negative samples far away by effectively weighting informative pairs. It works with three types of similarities:

• Self-Similarity (the distance between the negative sample and anchor)
• Positive-Similarity (the relationship between positive pairs)
• Negative-Similarity (the relationship between negative pairs)

Finally, it is also worth noting, that in fact, you don’t need to use only one loss function, but you can combine multiple loss functions. For example, you can use the Triplet Loss function with CrossEntropy and MultiSimilarity or N-pair together with Angular Loss. This should often lead to better results than the standalone loss function.

2) Unsupervised Learning

AutoEncoder

Unsupervised learning is helpful when we have a completely unlabelled dataset, meaning we don’t know the classes of our images. These methods are very interesting because the annotation of data can be very expensive and time-consuming. The most simplistic unsupervised learning can simply use some form of AutoEncoder.

AutoEncoder is a neural network consisting of two parts: an encoder, which encodes the image to the smaller representation (embedding vector), and a decoder, which is trying to reconstruct the original image from the embedding vector.

After the whole model is trained, and the decoder is able to reconstruct the images from smaller vectors, the decoder part is discarded and only the encoder part is used in similarity search engines.

There are many other solutions for unsupervised learning. For example, we can train AutoEncoder architecture to colourize images. In this technique, the input image has no colour and the decoding part of the network tries to output a colourful image.

Image Inpainting

Another technique is Image Inpainting, where we remove part of the image and the model will learn to inpaint them back. Interesting way to propose a model that is solving jigsaw puzzles or correct ordering of frames of a video.

Then there are more advanced unsupervised models like SimCLR, MoCo, PIRL, SimSiam or GAN architectures. All these models try to internally represent images so their outputs (vectors) can be used in visual search systems. The explanation of these models is beyond this article.

Tips for Training Deep Metric Models

Here are some useful tips for training deep metric learning models:

• Batch size plays an important role in deep metric learning. Some methods such as N-pair should have bigger batch sizes. Bigger batch sizes generally lead to better results, however, they also require more memory on the GPU card.
• If your dataset has a bigger variation and a lot of classes, use a bigger batch size for Multi-similarity loss.
• The most important part of metric learning is your data. It’s a pity that most research, as well as articles, focus only on models and methods. If you have a large collection with a lot of products, it is important to have a lot of samples per product. If you have fewer classes, try to use some unsupervised method or cross-entropy loss and do heavy augmentations. In the next section, we will look at data in more depth.
• Try to start with a pre-trained model and tune the learning rate.
• When using Siamese or Triplet training, try to play with the margin term, all the modern frameworks will allow you to change it (make it harder) during the training.
• Don’t forget to normalize the output of the embedding if the loss function requires it. Because we are comparing vectors, they should be normalized in a way that the norm of the vectors is always 1. This way, we are able to compute Euclidean or cosine distances.
• Use advanced methods such as MultiSimilarity with big batch size. If you use Siamese, Triplet, or N-pair, mining of negatives or positives is essential. Start with easier samples at the beginning and increase the challenging samples every epoch.

Neural Text Search on Images with CLIP

Up to right now, we were talking purely about images and searching images with image queries. However, a common use case is to search the collection of images with text input, like we are doing with Google or Bing search. This is also called Text-to-Image problem, because we need to transform text representation to the same representation as images (same vector space). Luckily, researchers from OpenAI develop a simple yet powerful architecture called CLIP (Contrastive Language Image Pre-training). The concept is simple, instead of training on pair of images (SIAMESE, NPAIR) we are training two models (one for image and one for text) on pairs of images and texts.

You can train a CLIP model on a dataset and then use it on your images (or videos) collection. You are able to find similar images/products or try to search your database with a text query. If you would like to use a CLIP-like model on your data, we can help you with the development and integration of the search system. Just contact us at care@ximilar.com, and we can create a search system for your data.

The Training Data
for Visual Search Engines

99 % of the deep learning models have a very expensive requirement: data. Data should not contain any errors such as wrong labels, and we should have a lot of them. However, obtaining enough samples can be a problematic and time-consuming process. That is why techniques such as transfer learning or image augmentation are widely used to enrich the datasets.

How Does Image Augmentation Help With Training Datasets?

Image augmentation is a technique allowing you to multiply training images and therefore expand your dataset. When preparing your dataset, proper image augmentation is crucial. Each specific category of data requires unique augmentation settings for the visual search engine to work properly. Let’s say you want to build a fashion visual search engine based strictly on patterns and not the colours of items. Then you should probably employ heavy colour distortion and channel-swapping augmentation (randomly swapping red, green, or blue channels of an image).

On the other hand, when building an image search engine for a shop with coins, you can rotate the images and flip them to left-right and upside-down. But what to do if the classic augmentations are not enough? We have a few more options.

Removing or Replacing Background

Most of the models that are used for image search require pairs of different images of the same object. Typically, when training product image search, we use an official product photo from a retail site and another picture from a smartphone, such as a real-life photo or a screenshot. This way, we get a pair-based model that understands the similarity of a product in pictures with different backgrounds, lights, or colours.

All such photos of the same product belong to an entity which we call a Similarity Group. This way, we can build an interactive tool for your website or app, which enables users to upload a real-life picture (sample) and find the product they are interested in.

Background Removal Solution

Sometimes, obtaining multiple images of the same group can be impossible. We found a way to tackle this issue by developing a background removal model that can distinguish the dominant foreground object from its background and detect its pixel-accurate position.

Once we know the exact location of the object, we can generate new photos of products with different backgrounds, making the training of the model more effective with just a few images.

The background removal can also be used to narrow the area of augmentation only to the dominant item, ignoring the background of the image. There are a lot of ways to get the original product in different styles, including changing saturation, exposure, highlights and shadows, or changing the colours entirely.

Building such an augmentation pipeline with background/foreground augmentation can take hundreds of hours and a lot of GPU resources. That is why we deployed our Background Removal solution as a ready-to-use image tool.

You can use the Background Removal as a stand-alone service for your image collections, or as a tool for training data augmentation. It is available in public demo, App, and via API.

GAN-Based Methods for Generating New Training Data

One of the modern approaches is to use a Generative Adversarial Network (GAN). GANs are incredibly powerful in generating whole new images from some specific domain. You can simply create a model for generating new kinds of insects or making birds with different textures.

The greatest advantage of GAN is you will easily get a lot of new variants, which will make your model very robust. GANs are starting to be widely used in more tasks such as simulations, and I think the gathering of data will cost much less in the near future because of them. In Ximilar, we used GAN to create a GAN Image Upscaler, which adds new relevant pixels to images to increase their resolution and quality.

When creating a visual search system on our platform, our team picks the most suitable neural network architecture, loss functions, and image augmentation settings through the analysis of your visual data and goals. All of these are critical for the optimization of a model and the final accuracy of the system. Some architectures are more suitable for specific problems like OCR systems, fashion recommenders or quality control. The same goes with image augmentation, choosing the wrong settings can destroy the optimization. We have experience with selecting the best tools to solve specific problems.

Annotation System for Building Image Search Datasets

As we can see, a good dataset definitely is one of the key elements for training deep learning models. Obtaining such a collection can be quite expensive and time-consuming. With some of our customers, we build a system that continually gathers the images needed in the training datasets (for instance, through a smartphone app). This feature continually & automatically improves the precision of the deployed search engines.

How does it work? When the new images are uploaded to Ximilar Platform (through Custom Similarity service) either via App or API, our annotators can check them and use them to enhance the training dataset in Annotate, our interface dedicated to image annotation & management of datasets for computer vision systems.

Annotate effectively works with the similarity groups by grouping all images of the same item. The annotator can add the image to a group with the relevant Stock Keeping Unit (SKU), label it as either a product picture or a real-life photo, add some tags, or mark objects in the picture. They can also mark images that should be used for the evaluation and not used in the training process. In this way, you can have two separate datasets, one for training and one for evaluation.

We are quite proud of all the capabilities of Annotate, such as quality control, team cooperation, or API connection. There are not many web-based data annotation apps where you can effectively build datasets for visual search, object detection, as well as image recognition, and which are connected to a whole visual AI platform based on computer vision.

How to Improve Visual Search Engine Results?

We already assessed that the optimization algorithm and the training dataset are key elements in training your similarity model. And that having multiple images per product then significantly increases the quality of the trained similarity model. The model (CNN or other modern architecture) for similarity is used for embedding (vector) extraction, which determines the quality of image search.

Over the years that we’ve been training visual search engines for various customers around the world, we were also able to identify several potential weak spots. Their fixing really helped with the performance of searches as well as the relevance of the search results. Let’s take a look at what can improve your visual search engine:

Include Tags

Adding relevant keywords for every image can improve the search results dramatically. We recommend using some basic words that are not synonymous with each other. The wrong keywords for one item are for instance “sky, skyline, cloud, cloudy, building, skyscraper, tall building, a city”, while the good alternative keywords would be “sky, cloud, skyscraper, city”.

Our engine can internally use these tags and improve the search results. You can let an image recognition system label the images instead of adding the keywords manually.

Include Filtering Categories

You can store the main categories of images in their metadata. For instance, in real estate, you can distinguish photos that were taken inside or outside. Based on this, the searchers can filter the search results and improve the quality of the searches. This can also be easily done by an image recognition task.

Include Dominant Colours

Colour analysis is very important, especially when working for a fashion or home decor shop. We built a tool conveniently called Dominant Colors, with several extraction options. The system can extract the main colours of a product while ignoring its background. Searchers can use the colours for advanced filtering.

Use Object Detection & Segmentation

Object detection can help you focus the view of both the search engine and its user on the product, by merely cutting the detected object from the image. You can also apply background removal to search & showcase the products the way you want. For training object detection and other custom image recognition models, you can use our App & Annotate.

Use Optical Character Recognition (OCR)

In some domains, you can have products with text. For instance, wine bottles or skincare products with the name of the item and other text labels that can be read by artificial intelligence, stored as metadata and used for keyword search on your site.

Improve Image Resolution

If the uploaded images from the mobile phones have low resolution, you can use the image upscaler to increase the resolution of the image, screenshot, or video. This way, you will get as much as possible even from user-generated content with potentially lower quality.

Combine Multiple Approaches

Fusion – Combining multiple features like model embeddings, tags, dominant colours, and text increases your chances to build a solid visual search engine. Our system is able to use these different modalities and return the best items accordingly. For example, extracting dominant colours is really helpful in Fashion Search, our service combining object detection, fashion tagging & visual search.

Search Engine and Vector Databases

Once you trained your model (neural network), you can extract and store the embeddings for your multimedia items somewhere. There are a lot of image search engine implementations that are able to work with vectors (embedding representation) that you can use. For example, Annoy from Spotify or FAISS from Facebook developers.

These solutions are open-source (i.e. you don’t have to deal with usage rights) and you can use them for simple solutions. However, they also have a few disadvantages:

• After the initial build of the search engine database, you cannot perform any update, insert or delete operations. Once you store the data, you can only perform search queries.
• You are unable to use a combination of multiple features, such as tags, colours, or metadata.
• There’s no support for advanced filtering for more precise results.
• You need to have an IT background and coding skills to implement and use them. And in the end, the system must be deployed on some server, which brings additional challenges.
• It is difficult to extend them for advanced use cases, you will need to learn a complex codebase of the project and adjust it accordingly.

Building a Visual Search Engine on a Machine Learning Platform

The creation of a great visual search engine is not an easy task. The mentioned challenges and disadvantages of building complex visual search engines with high performance are the reasons why a lot of companies hesitate to dedicate their time and funds to building them from scratch. That is where AI platforms like Ximilar come into play.

Custom Similarity Service

Ximilar provides a computer vision platform, where a fast similarity engine is available as a service. Anyone can connect via API and fill their custom collection with data and query at the same time. This streamlines the tedious workflow a lot, enabling people to have custom visual search engines fast and, more importantly, without coding. Our image search engines can handle other data types like videos, music, or 3D models. If you want more privacy for your data, the system can also be deployed on your hardware infrastructure.

In all industries, it is important to know what we need from our model and optimize it towards the defined goal. We developed our visual search services with this in mind. You can simply define your data and problem and what should be the primary goal for this similarity. This is done via similarity groups, where you put the items that should be matched together.

Examples of Visual Search Solutions for Business

One of the typical industries that use visual search extensively is fashion. Here, you can look at similarities in multiple ways. For instance, one can simply want to find footwear with a colour, pattern, texture, or shape similar to the product in a screenshot. We built several visual search engines for fashion e-shops and especially price comparators, which combined search by photo and recommendations of alternative similar products.

Based on a long experience with visual search solutions, we deployed several ready-to-use services for visual search: Visual Product Search, a complex visual search service for e-commerce including technologies such as search by photo, similar product recommendations, or image matching, and Fashion Search created specifically for the fashion segment.

Another nice use case is also the story of how we built a Pokémon Trading Card search engine. It is no surprise that computer vision has been recently widely applied in the world of collectibles. Trading card games, sports cards or stamps and visual AI are a perfect match. Based on our customers’ demand, we also created several AI solutions specifically for collectibles.

The Workflow of Building
a Visual Search Engine

If you are looking to build a custom search engine for your users, we can develop a solution for you, using our service Custom Image Similarity. This is the typical workflow of our team when working on a customized search service:

1. Setup, Research & Plan – Initial calls, the definition of the project, NDA, and agreement on expected delivery time.

2. Data – If you don’t provide any data, we will gather it for you. Gathering and curating datasets is the most important part of developing machine learning models. Having a well-balanced dataset without any bias to any class leads to great performance in production.

3. First prototype – Our machine learning team will start working on the model and collection. You will be able to see the first results within a month. You can test it and evaluate it by yourself via our clickable front end.

4. Development – Once you are satisfied with the results, we will gather more data and do more experiments with the models. This is an iterative way of improving the model.

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. We will show you how to connect and work with the API for visual similarity (insert, delete, search endpoints).

If you are interested in knowing more about how the cooperation with Ximilar works in general, read our How it works and contact us anytime.

We are also able to do a lot of additional steps, such as:

• Managing and gathering more training data continually after the deployment to gradually increase the performance of visual similarity (the usage rights for user-generated content are up to you; keep in mind that we don’t store any physical images).
• Building a customized model or multiple models that can be integrated into the search engine.
• Creating & maintaining your visual search collection, with automatic synchronization to always keep up to date with your current stock.
• Scaling the service to hundreds of requests per second.

Visual Search is Not Only
For the Big Companies

I presented the basic techniques and architectures for training visual similarity models, but of course, there are much more advanced models and the research of this field continues with mile steps.

Search engines are practically everywhere. It all started with AltaVista in 1995 and Google in 1998. Now it’s more common to get information directly from Siri or Alexa. Searching for things with visual information is just another step, and we are glad that we can give our clients tools to maximise their potential. Ximilar has a lot of technical experience with advanced search technology for multimedia data, and we work hard to make it accessible to everyone, including small and medium companies.

If you are considering implementing visual search into your system:

1. Schedule a call with us and we will discuss your goals. We will set up a process for getting the training data that are necessary to train your machine learning model for search engines.

2. In the following weeks, our machine learning team will train a custom model and a testable search collection for you.

3. After meeting all the requirements from the POC, we will deploy the system to production, and you can connect to it via Rest API.

The post How to Build a Good Visual Search Engine? 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.