XimilarVisual AI platform for image recognition, similarity search, and automated tagging.

How does image recognition work?

Image recognition uses convolutional neural networks to extract features from images and map them to categories, attributes, or numerical values. These mappings are learned from labelled training examples. Related tasks such as OCR use similar mechanisms to extract text, while localisation models identify and classify multiple regions within a single image in a single inference pass. We provide a number of off‑the‑shelf solutions for classifying specific image data, such as stock photos, home decor and furniture images, fashion photos, or trading and collectible cards. Custom models can also be easily trained on our platform. A developer can access every capability via REST using Python or any HTTP client, apply compliance controls at the workspace level, and manage the full model lifecycle — from training through to production — without touching any infrastructure. Read the articles in our blog to learn about image recognition technology.

Asked by Bianca Ferreira · May 17, 2026

What is Visual Search?

Visual & similarity search technology can analyze the overall visual aesthetic of an image or detected object in an image, independent of the origin of images or metadata (such as keywords). It understands the concept of similarity according to your subjective perception. That is why it can provide the most relevant results to image queries, whether you look for the exact match or similar items.

Asked by Vikram Rao · May 1, 2026

Which collectibles can AI Recognition of Collectibles recognize?

As for now, the service is able to detect (and mark by bounding boxes) collectibles such as stamps, coins, banknotes, comic books and trading cards, as well as antique items. For collectible cards, the service can identify whether it is a Trading Card Game (Pokémon, Magic The Gathering – MTG, Yu‑Gi‑Oh!, Lorcana, Flesh and Blood and so on) or a Sports Card (Baseball, Basketball, Hockey, Football, Soccer, or MMA), with several additional features (e.g., signature). It can be easily customized to evaluate images based on your criteria. The full taxonomy of the identification results with all supported games and sports can be found in our documentation page. For comic books, the service can identify more than 1 million magazines, books and manga – via name, title, publisher, issue number and release date. The service is constantly expanding based on the requests from our customers.

Asked by Linda Petersen · Apr 15, 2026

How Ximilar streamlines image processing tasks and reduces costs?

Ximilar’s systems significantly reduce image processing costs by automating repetitive tasks such as analyzing, tagging and sorting images. This automation results in long‑term savings, allowing for continuous 24/7 addition of new visual content without additional metadata. We continually enhance our platform, enabling us to build services efficiently and quickly and to modify existing solutions to suit your needs. Costs and labour are reduced by utilizing a combination of pre‑trained and new models. Service use is billed via API credits, with a customizable monthly plan based on your consumption. Using our credit calculator helps you optimise cost‑effectiveness. For sudden system loads, you can add extra credit packs to your monthly credit supply. Once your solution is live, we can continually upgrade and enhance it, altering any component in the modular structure. Our feedback mechanisms help us understand which model performs best, allowing us to refine the solution. Ready‑to‑use solutions are routinely updated to stay industry‑relevant, and these upgrades come at no extra cost.

Asked by Xander de Vries · Apr 9, 2026

What are the typical Visual Search applications?

Visual search typically involves searching images or products using an image query, including photos from social media and user‑generated content, like smartphone photos. Our Search by Photo combines this technology with detecting individual products in images. One such solution is Search Fashion by Photo. The technology is frequently employed for similarity searches, particularly for product recommendations in e‑commerce. It assesses image or detected object features, like colour, edges, or patterns, to suggest the most similar alternatives. Another typical use is image & product matching. Since the technology can identify duplicates or nearly identical items in various images with different quality, it can help with curating product galleries and eliminating unnecessary content. While e‑commerce is the most common use case, visual search also provides possibilities in industrial sectors, scientific research, and security systems.

Asked by Yuki Kobayashi · Apr 1, 2026