Custom Image Recognition

Train your own image categorization, tagging & object detection models to manage complex image collections. Machine learning cannot be easier.

I want to:

Image Categorization & Tagging

MY OWN AI

Train custom Visual AI for image recognition

With the Categorization & Tagging service, you define custom categories & tags, upload training images from your domain, link them to these categories and tags, and train custom image recognition models.

This way, you can automate the quality control of your products, tagging, sorting, filtering, and even recommendations of the images from your collection.

DELEGATE ROUTINE TASKS TO AI

AI processes visual data consistently & efficiently in milliseconds

Enormous databases of pictures can be managed by AI running on Ximilar cloud, that can handle large volumes of data 24/7. You can access it via API and integrate it into your application.

Working with the Ximilar platform doesn’t require a specific skill set. You easily train, chain, and deploy your models, while we take care of the back-end.

CATEGORIZATION

Assign a category to each image

Image categorization assigns each image a category, such as a maxi dress or midi dress. Different categories should be visually distinctive, and each image should belong exactly to one category.

Categorization of Fashion by Ximilar

TAGGING

Tag every image with many tags

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

Tagging of Fashion by Ximilar

Enrich

your data with detailed information on images

Delegate

routine tasks to consistent AI trained on your data

Save

time and resources with AI automation

Custom Object Detection

TRAIN AI TO SPOT ANY OBJECT

Object Detection finds & marks objects from different categories

At Ximilar’s platform, you can train custom object detection models to identify any object, such as people, cars, particles in the water, imperfections of materials, objects of the same shape, size, or color.

An object detection task can either work independently or in a combination with categorization & tagging tasks that will tag the detected objects.

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.

One Platform – Two Interfaces

App

You can annotate your images in Ximilar App. Just click on the image and draw a bounding box.

TRY FOR FREE

Annotate

Annotate was built for the annotation of large image datasets with complex hierarchical taxonomies in a team.

READ MORE

Flows: Chain & Combine the Models

The key to the management of complex image databases is the interaction of more different models.
Chaining machine learning models with Flows

A HIERARCHY OF MODELS

Divide a complex problem into separate recognition, detection & tagging tasks

With Flows, you can combine the machine learning models and chain them in a sequence, that exactly matches the decision-making process that you would go through while working with your visual data.

A Flow is like a tree, that grows bigger with the hierarchy of your models. Each image travels through the tree until it is properly processed and tagged. Based on this hierarchy, you also get suggestions when annotating the images.

Flows in Annotate

UNIQUE MODULAR STRUCTURE

Make a network of models and change any part you need anytime

  • Re-train, add or remove any unit anytime
  • Combine custom and ready-to-use services
  • Recognize exclusively the detected objects
  • Call multiple recognition tasks in parallel
  • Call endless nested flows by a primary flow
  • Use one flow in several places
Read more

Build rich hierarchy of tasks

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

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

Images can then be gradually sorted with an increasing level of precision. The first task 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.

Image Regression

AI MODEL THAT PREDICTS VALUES

Predict size of objects, age of people, or rating from images

The image regression model is able to predict numerical values from a range that you define in your images.

Image regression is used for building quality control systems, and to estimate values such as age, size, worn out level, or rating. It is a great tool for real estate and insurance companies, MedTech, and Industry 4.0.

CONTACT US

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 paying for training time

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

No paying for idle time

No fees for the idle time either

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

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.

Read more

With the AI Explainability in Ximilar App, you can see which parts of your images are the most important to your image recognition models.
Read moreDecember 2021
We developed a computer vision system for object detection, counting, and tracking on Nvidia Jetson Nano.
Read moreOctober 2021
Flows is a service for combining machine learning models for image recognition, object detection and other AI services into API.
Read moreSeptember 2021

Interested in ready-to-use solutions?

Check our ready-to-use Image Recognition services tailored for Fashion, Home decor, and Stock Photos.

DISCOVER SERVICES

API Documentation

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

FULL DOCUMENTATION