Computer Vision Platform

A unified no-code machine learning platform for the training of image classification & object detection models.

I want to:

Image Classification

Models for automatic recognition, categorization & tagging of images or objects in them.

CATEGORIZATION & TAGGING

Train Your Own Visual AI

Image categorization & tagging used for quality control powered by Ximilar AI.

Define your own categories & tags, link them to training images, and train custom image recognition models.

Automate all image classification with computer vision: tagging, sorting, filtering, and even quality control or recommendations of the items or images from your collection.

DELEGATE ROUTINE TASKS TO AI

No-Code Machine Learning

Computer vision platform of Ximilar is accessible through App and via API.

Working with Ximilar computer vision platform doesn’t require coding skills. You easily train & chain your models with a few clicks.

AI running on Ximilar cloud processes large volumes of data 24/7. You can connect via API and integrate both ready-to-use and custom models into your system.

Enrich

your data with detailed information

Ximilar – Delegate routine image-processing task a consistent visual AI trained on your data.

Delegate

routine tasks to consistent AI

Save time and expenses with automatic image-processing solutions powered by Ximilar computer vision.

Save

time and resources with automation

WHAT IS CATEGORIZATION?

Assign a category to each image

Categorization of Fashion by Ximilar

Image categorization assigns each image a category, such as a maxi dress or midi dress. The categories are visually distinctive, and each image belongs only to one category.

WHAT IS TAGGING?

Tag every image with many tags

Categorization of Fashion by Ximilar

Define a set of tags for the features & objects that should be recognized in your images, and train a custom tagging model able to provide tags for each image in your collection.

Skip the setup with ready-to-use solutions

Check out our solutions for fashion, home decor, collectibles, and more.
They can be used right away or combined with custom models.

Image Regression

A specialized recognition system for evaluation or grading.

IMAGE REGRESSION

Automatic Prediction of Size, Age, or Rating From Images

Image regression means value prediction from images powered by AI.

The image regression predicts numerical values within a defined range from your images. It is used in quality control, and to estimate values such as age, size, worn-out level, or rating.

You can train regression models under Image Classification in our App (create a new task: regression). We can also build a value prediction system tailored to your use case.

Object Detection

Object detection automatically finds different types of objects & marks them with bounding boxes.

OBJECT DETECTION

Train AI to Spot Any Object

Object detection and counting powered by Ximilar computer vision platform.

Train custom object detection models (CenterNet) to identify any object, such as people, cars, particles in the water, imperfections of materials, or objects of the same shape, size, or colour.

Object detection can work both independently or combined with other tasks, such as automatic tagging.

Q & A

How do I prepare the training data?

The training of object detection models requires bigger datasets and more training time. It begins with data annotation – the manual marking of objects with bounding boxes. You can use the same dataset as for Categorization & Tagging model training.

Q & A

How do you work with my data?

During the training of custom image recognition models, your annotated images are divided into two groups. Apart from the training set, there is a smaller validation set, which is used to evaluate the accuracy of the model before the deployment. You can also upload another independent test set.

Data Annotation Tools

Ximilar App smal image

App

You can annotate your training images directly in Ximilar App, where you train the models.

Annotate

Level up your data annotation work with a professional tool for quick annotation in a team.

Flows: Combine Your Models

The key to the management of complex image databases is the interaction of more different models.

MODELS WORKING TOGETHER

Divide Complex Problems Into Simple Tasks

Chaining machine learning models with Flows

With Flows, the machine learning models can be combined and chained in a sequence.

Each image travels through the sequence of your models until it is properly processed and tagged. Based on Flows, you also get suggestions when annotating the images.

ENDLESS POSSIBILITIES

Change & Modify Your Tasks Anytime

Flows in Annotate
  • Combine custom & ready-to-use solutions
  • Re-train, add or remove any unit
  • Recognize only the detected objects
  • Call more tasks (models) in one API call or multiple recognition tasks in parallel
  • Add endless nested flows into a primary flow
  • Use one flow in several places

Build rich hierarchy

Define a flow with a few clicks, then use it for both training & automation

Play with the features

Add, remove, or change components, duplicate & modify your flows

Make changes on the fly

Flow structure handles any changes to both dataset and connected models

EXAMPLE: REAL ESTATE

Conditional image processing

A machine learning model flow used as a branch selector by Ximilar.

Imagine you are building a real estate website. The first models in your flow can filter out all images that don’t meet certain selection criteria. In this case, it would be the pictures without any real estate, rooms, or furnishing.

EXAMPLE: REAL ESTATE

Automatic filtering, sorting & tagging

A machine learning model flow used as a branch selector by Ximilar.

Images can then be gradually sorted with an increasing level of precision. The first task (model) separates apartments and houses. Then, the apartments are sorted by room type, design, and furniture decor, and the houses by features such as architecture, area, garden or swimming pool.

Be Ahead of the Competition

Unlimited number of images

There are no limits on number of images per model/label

Use one image for many models

You can use the same images for the training of different models

Built-in data augmentation

You don’t have to prepare or multiply the training data in advance

No fees for training time

Unlike the competition, Ximilar doesn’t charge you for the training time

No fees for idle time

The same goes for idle time – you don’t pay anything

Cashing deployed models

Image processing takes 300 ms, as opposed to 2-3 s at other platforms

TECHNOLOGY STACK

We use state of the art neural network models & machine learning techniques

TECHNOLOGY STACK

Our AI is improving constantly, so you always have up-to-date technology. Each model has millions of parameters that can be processed by CPU or GPU.

Our intelligent algorithm picks and uses the best performing models. We are using the latest technologies for machine learning as TensorFlow or OpenVINO.

Frequently Asked Questions

What means categorization, tagging, tags, and labels? What is the difference between categorization and tagging?

Is the number of labels per task limited?

Can I combine machine learning models or put them in a sequence?

What is image annotation, and when do I need it?

What is Annotate? How does it work?

What is the difference between annotation in Ximilar App and Annotate?

Does Annotate support work in a team or multiple accounts?

How do I test the accuracy of my Image Recognition and Object Detection models? What are evaluation metrics?

What is A/B testing of machine learning models?

What is mAP metric of object detection models?

Is there a difference between a task and a model?

What is a machine learning loop?

What is custom image recognition?

What is the use of image recognition in retail?

In which fields does image recognition help?

What factors determine the accuracy of the image recognition system?

What image classification techniques does the Ximilar platform offer?

How image recognition works?

What are Flows?

Which services can I combine with Flows? What are Actions?

Can I use one task (model) in multiple Flows?

What is the use of image recognition in healthcare?

Can I have multiple Flows?

Is training, deployment and using of my Image Recognition tasks charged?

I know what I need, but I’m not sure how to build a Flow.

What do I pay for when using the Flows?

How fast and efficient is the image recognition process?

Tips & Tricks

Ximilar App is a way to access computer vision solutions without coding and to gain your own authentication key to use them via API.

Getting Started with Ximilar App: Plan Setup & API Access

Ximilar App is a way to access computer vision solutions without coding and to gain your own authentication key to use them via API.

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Get Image Recognition API Now

We take care of the complexity behind and wrap it in a few lines of code.

Full documentation
cURL
Python
PHP
curl -H "Content-Type: application/json" -H "authorization: Token __API_TOKEN__" https://api.ximilar.com/recognition/v2/classify -d '{"task_id": "__TASK_ID__", "version": 2, "descriptor": 0, "records": [ {"_url": "https://bit.ly/2IymQJv" } ] }'

Ximilar is a reliable & responsible partner in image AI. We deliver what we promise.

Contact us now
  • Easy setup
  • Expert team
  • Fast scaling