Computer Vision Platform

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

I want to:

CATEGORIZE & TAG
PREDICT VALUES
DETECT OBJECTS
COMBINE MODELS

Image Classification

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

CATEGORIZATION & TAGGING

Train Your Own Visual 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.

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

DELEGATE ROUTINE TASKS TO AI

No-Code Machine Learning

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.

START NOW – FREE

Enrich

your data with detailed information

Delegate

routine tasks to consistent AI

Save

time and resources with automation

WHAT IS CATEGORIZATION?

Assign a category to each image

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

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.

EXPLORE

Image Regression

A specialized recognition system for evaluation or grading.

IMAGE REGRESSION

Automatic Prediction of Size, Age, or Rating From Images

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

Train custom object detection models 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.

how to train object detection model?

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

App

You can do the annotation directly in our App, where you train the models.

TRY FOR FREE

Annotate

Level up your annotation work with a professional annotation tool for teams.

Flows: Combine Your Models

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

Chaining machine learning models with Flows

MODELS WORKING TOGETHER

Divide Complex Problems Into Simple Tasks

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

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

flows – guide

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

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.

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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.

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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.

Frequently Asked Questions

Read all FAQ

Tips & Tricks

API Documentation

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

FULL DOCUMENTATION

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" } ] }'

import requests 
import json 
import base64 
 
url = 'https://api.ximilar.com/recognition/v2/classify/' 
headers = { 
    'Authorization': "Token __API_TOKEN__", 
    'Content-Type': 'application/json' 
} 
with open(__IMAGE_PATH__, "rb") as image_file: 
    encoded_string = base64.b64encode(image_file.read()).decode('utf-8') 
 
data = { 
    'task_id': __TASK_ID__, 
    'records': [ {'_url': __IMAGE_URL__ }, {"_base64": encoded_string } ] 
} 
 
response = requests.post(endpoint, headers=headers, data=json.dumps(data)) 
if response.raise_for_status(): 
    print(json.dumps(response.json(), indent=2)) 
else: 
    print('Error posting API: ' + response.text)

$curl_handle = curl_init("https://api.ximilar.com/recognition/v2/classify");

$data = [
    'task_id' => __TASK_ID__,
    'records' => [
        [ '_url' => 'https://bit.ly/2IymQJv' ],
        [ '_base64' => base64_encode(file_get_contents(__PATH_TO_IMAGE__)) ]
    ]
];

curl_setopt($curl_handle, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($curl_handle, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($curl_handle, CURLOPT_RETURNTRANSFER, true);
curl_setopt($curl_handle, CURLOPT_FAILONERROR, true);
curl_setopt($curl_handle, CURLOPT_HTTPHEADER, array(
        "Content-Type: application/json",
        "Authorization: Token __API_TOKEN__",
        "cache-control: no-cache",)
);

$response = curl_exec($curl_handle);
$error_msg = curl_error($curl_handle);

if ($error_msg) { // Handle error
    print_r($error_msg);
} else { // Handle response
    print_r($response);
}
curl_close ($curl_handle);