How it Works

Artificial Intelligence in an easy to use package. Smart machine learning techniques improved to the max. Tools for quick use.
Ico Bullseye

High Accuracy

Use of deep learning algorithms with the highest accuracy & extra tweaks.

Ico Clipboard

Simple Setup

Implement cutting-edge vision automation quick. Without any development costs.

Ico Infinite

Recognize Anything

Train custom neural network to recognize your specific type of imagery.

Ico Cake

Clear Interface

Create powerful and custom image recognizers in an intuitive web app.

Ico Shield

Ongoing Updates

We always improve the underlying machine learning algorithms so you get the state of the art tools.

Ico Infinite

Speed & Scalability

Recognize and search millions of images — we are good friends with Big Data.

Easy to Use Interface

User interface to create, manage & deploy your custom image recognition models. No machine learning or coding knowledge required.

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Artificial Intelligence at Work

fashion

industries

real-estate

surveillance

stock photo

healthcare

e-commerce

We are Experts
Ready to Help You Out

Machine Learning is our passion. We know all the ins & outs of technologies that might help you level-up your business using full or partial automation. Feel free to contact us and lets speak about what we can do for you.

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Setup your Custom Recognition in 5 minutes

1Define

Define your categories and upload sample images.

Picture of Define

2Train

Define your categories and upload sample images.

Picture of Train

3Recognize

Define your categories and upload sample images.

Picture of Recognize

API Documentation

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

full documentation 
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);
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