Computer Vision Platform

Image recognition is the technology that analyzes an image and describes its content — categorizing it, tagging its attributes, or locating specific objects within it.

Ximilar provides two options:

1. Choose from off-the-shelf solutions for the detection, recognition, tagging, and sorting of specific image data, such as stock photos, home decor and furniture images, fashion photos, or trading and collectible cards.
2. Train your own custom image recognition models on Ximilar's computer vision platform without coding. Namely, you can train:

• image classification models: categorization & tagging, image regression (value prediction)
• object detection models

The custom models can be easily combined with existing ready-to-use solutions with Flows. Object detection requires manual annotation of training data, which can be done in the dedicated interface Annotate. The result is a full suite of visual capabilities that can be assembled, tested, and available in days rather than months.

Custom image recognition systems underpin computer vision pipelines across retail, healthcare, security, manufacturing, and beyond. Related capabilities such as OCR extend this further, extracting text from images for document processing and product data workflows.

The system supports single-category classification, multi-attribute tagging, and value regression — all accessible from a unified environment.

Every model type shares the same training, evaluation, and deployment pipeline, so switching between them requires no additional configuration.

Each model is automatically evaluated on an evaluation dataset during training. You can also upload a separate test set for independent measurement.

In the App, navigate to your task, open the model detail view, and review evaluation metrics — precision, recall, confusion matrix, and failed images — giving you transparent, actionable insight into model output. Based on these metrics, you can further iterate and improve your training dataset to make your model more robust. We are also able to change the architecture of the neural network if needed and improve the robustness for your specific data.

You can also test a model on any individual image by dragging and dropping it or pasting a URL into the Test panel under your service.

A/B testing lets you compare the performance of a new model version against the current production build using real traffic or held-out data. It is the recommended way to validate improvements before committing to a new deployment, ensuring every update is a measurable step forward.

The mAP, or mean average precision (AP) is the standard evaluation metric describing the precision of your object detection models. It combines precision, recall, the precision-recall curve, average precision, and IOU (the algorithm that measures bounding box overlap) into a single score per label.

You can find mAP per category in your workspace under Object Detection > Tasks > Models in the model detail view.

Basic categorization and recognition models typically process an image in 5 to 100 milliseconds, depending on input resolution and CDN speed. Cached model serving eliminates cold-start delays — the quickest response is always ready. For high-throughput deployments, models can be further optimised to get the most from dedicated hardware.

Task refers to the type of problem being solved — image classification, object recognition, or regression. It defines the category set and training configuration. In our documentation, a task represents the starting point for your machine learning project. It serves as the abstract definition for training a recognition model and includes a set of labels, which can each be assigned to multiple training images. Your tasks, data, and images are private and accessible only to you.

Model is the trained result (of a task) — a deep model trained on your images and ready for inference. Each model includes an accuracy metric measured at the end of training.

Models are private to their owner, and each time you retrain, a new model version is created with an incremented version number. You can choose which version to deploy in production. Read How Ximilar technology works? for details.

Any image processed by a deployed machine learning model can be saved to the workspace and used to retrain the model to improve it. The retraining is done manually after annotators check these new images. Then the new, better and more accurate version of the model is deployed. This loop improves the accuracy of your model in the long term, especially if the character of the data changes over time (e.g. the lightning of the scene changes dramatically). See pricing for details about availability.

No. Training custom models is completely free, as is deploying them. Ximilar charges neither for training time nor for idle time — only for actual inference. This makes it cost-efficient for enterprise teams running continuous updates and for smaller teams still validating their model before committing to full-scale rollout.

In retail, image recognition is pivotal in optimizing operations depending on visual data processing. One significant application lies in inventory management, where it automates tracking products and stock levels, streamlining restocking processes and minimizing manual effort.

Additionally, image recognition helps with consumer research, enabling retailers to gain insights into customer demographics and behaviour within physical stores. This information aids in optimizing store layouts, product placements, and staffing strategies to enhance the overall shopping experience.

Image recognition also supports personalized marketing initiatives by analyzing customer preferences and purchase history, allowing e-shops to tailor promotions and recommendations accordingly. This personalized shopping experience fosters stronger customer engagement and increases sales.

In many of these applications, image recognition works in tandem with visual search technology, which identifies visually similar products to items detected in product photos and real-life images.

Image recognition technology finds widespread application in diverse fields such as healthcare, retail, and security systems.

In healthcare, it aids in the interpretation of medical images, assisting clinicians in diagnosing diseases and identifying anomalies with greater precision. Read about some of our use cases here.

Similarly, in retail, image recognition streamlines checkout processes and, together with visual search, enhances customer experience through personalized recommendations.

In security, it strengthens surveillance systems by enabling real-time monitoring, threat detection, and facial recognition.

This technology is also essential for autonomous vehicles, enabling them to perceive their surroundings through cameras and sensors, recognize objects, pedestrians, and road signs, and make real-time decisions for safe navigation.

Additionally, image recognition systems help in both research and applied sciences. For instance, in biological research, microscopy image analysis and wildlife conservation. It plays a crucial role in monitoring and protecting endangered species. It enables researchers and conservationists to analyze vast amounts of camera trap data efficiently, identifying and tracking individual animals, assessing population dynamics, and detecting potential threats such as poaching or habitat loss.

Image recognition aids satellite imagery analysis, especially in monitoring vegetation coverage crucial for sectors like insurance and agriculture. LAICA, by World From Space (WFS) and Ximilar, addresses this, using deep learning to merge satellite data for daily vegetation monitoring despite cloud cover challenges.

In social media, image recognition facilitates image tagging and content moderation.

The two key factors determining the accuracy of the image recognition task are the complexity of the task and the quality and quantity of the training data available.

Clean, consistently labelled data matters more than algorithm choices alone. For tasks such as OCR, additional pre-processing steps can be used to optimize input quality. Ximilar makes it straightforward to evaluate, iterate, and optimize your models over time.

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.

Image recognition helps optimize diagnostics, treatment, as well as patient care by employing advanced AI algorithms to identify anomalies, recognize tissue features, or flag cases for clinician review.

It facilitates early disease detection, personalized treatment plans, and efficient workflows for healthcare providers. Key applications include diagnostic imaging and disease detection, such as analyzing X-ray or microscopy images, as well as providing surgical assistance. Additionally, the technology helps with other vital use cases such as drug discovery and health data analysis.

Models run on shared cloud servers, enabling hospitals and research institutions to access advanced recognition capabilities without building and maintaining their own model stack.

Automatic categorization is a process in which every image is assigned to a single category by a trained AI algorithm. The categories are visually distinctive — for example, dress vs. trousers. E-shops and enterprise catalogues typically use hierarchical taxonomies, working with both categories and subcategories.

Automatic image tagging assigns multiple attributes to each image — colour, pattern, design, style, material, and length. A dress image might be categorized as a casual dress and tagged with a rich set of descriptors simultaneously. This means even large collections can be fully enriched without manual effort.

In the context of categorization and tagging, a label describes both categories and tags. Object recognition works with bounding-box labels that identify located objects or people within a scene. Ximilar provides ready-to-use computer vision services such as Fashion Tagging and Home Decor Tagging, as well as a complete system for training custom models from scratch.

Log in to the Ximilar App and follow our step-by-step guide. No technical background is required — the system is designed so that domain experts, not just specialists, can train, evaluate, and deploy production-ready models.

Technically, no. However, using hundreds of categories requires a correspondingly large image collection. For complex taxonomies, we recommend building a hierarchy of models and connecting them with Flows — this is how enterprise-scale systems are structured. Each element can be retrained and updated independently.

Yes. Flows help you to chain models in a sequence, combine them in parallel, put them in a hierarchical structure, and implement conditional logic. This is the core principle behind flexible visual recognition on Ximilar, accommodating new capabilities without disrupting existing workflow.

Object recognition model training requires a labelled image collection. Labelling images means drawing bounding boxes around the objects that need to be located — precise and consistent markup is the single biggest determinant of recognition performance. In the Ximilar App, you can mark up images directly with Annotate, the advanced labelling tool built into the App.

Annotate is an advanced image labelling tool by Ximilar, fully integrated into the Ximilar App. It is built for fast, precise labelling of large training collections at speed. It shares the same back-end and database as the rest of the App — so every mark-up and verification is instantly reflected across your workspace.

You can upload images through the App and mark them up in Annotate. Assign jobs to teammates, set verification requirements, and track progress — all within the same environment you use for training and rollout. A clean, end-to-end process from raw data to production.

Both share the same core principle: view an image, select the recognition task, check or draw bounding boxes, and assign categories from your hierarchy. Both can be used to create and train tasks — they are two modes within the same computer vision toolset, not separate products.

The App excels at entity creation, data upload, model training, and bulk actions. Annotate is optimised for processing large volumes of training images precisely and fast, with intelligent job queues and multi-user verification — giving team leads full insight into labelling progress and consistency.

For large projects where a category hierarchy is already in place, upload images in the App and switch to Annotate for the actual labelling work.

Yes. Your company account can have multiple workspaces, each isolated for a separate project. Team members get access to the workspaces relevant to their work, and the workspace switcher in the top right corner of the app applies everywhere in the workspace. Workspaces are also accessible via REST for programmatic data management — useful for teams managing labelling across multiple server environments.

Flows are Ximilar's technology for chaining, combining, and structuring models into scalable, production-ready systems. Flows help you assemble complex image processing logic without writing orchestration code — and without managing the servers that run it.

Flows were made to combine different tasks into complex image processing systems.

A Flow is assembled from the following action types:

• Branch Selector – routes images through different branches based on recognition results
• Recognition – runs a classification task and returns structured results
• Object locator – runs a recognition task and returns bounding boxes and categories
• Object Selector – isolates detected objects for independent downstream processing
• Ximilar Service – calls any ready-to-use visual service
• List – runs multiple actions in sequence or in parallel
• Nested Flow – calls another Flow

Yes. A single trained model can be referenced by multiple Flows. Add the appropriate action type and select your task — no duplication of training data or model resources required.

Flows are available on all pricing plans. The number of Flows you can create depends on your subscription tier — check our Pricing page for details.

Need help with setup? Watch the video tutorial or read the guide below. For teams that need a production-ready system without the setup overhead, we can prepare a demo on your own data and deploy the full pipeline from labelling through to REST access.

Creating Flows and chaining tasks costs nothing. Credits are consumed only when inference is performed — when your models process images. Plan-level limits apply to the number of Flows; check our Pricing page to find the right tier.