How we Help

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

High Accuracy

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

Simple Setup

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

Recognize Anything

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

Clear Interface

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

Ongoing Updates

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

Speed & Scalability

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

Easy to Use Interface

User interface to manage different services. For instance, you can create, manage & deploy your custom image recognition models. No machine learning or coding knowledge required.

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Visual AI that Helps Many Businesses

fashion

e-commerce

real-estate

stock photo

surveillance

medtech

industries

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": "0a8c8186-aee8-47c8-9eaf-348103feb14d", "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);

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