Image Recognition

Categorize & tag images with your own labels. Use sleek user interface or rest API to create tasks. From problem definition to production in minutes.
  • Recognize
  • Any image
  • Custom training set
Play with Image Classification. Try free Web App Try it now

What does Image Classification mean in Machine Learning context

Machine learning gives computers the ability to learn without being explicitly programmed. You don’t need to be rocket scientist to create machine learning models. Define your task to predict labels, upload your data to system and click one button.

It does not matter if you need to distinguish cats from dogs or compare types of cancer cells. Our models can handle hundreds of labels & predict several images in a second. And if you need a bigger throughput, contact us and we would show you the universe of possibilities delivered by AI.

Coffee

Beverage

red structure

blue structure

safe state

Simple binary classification — drink assigned to a particular Label based on a few hundred of training images of the same kind that you supply to the Vize app.

Multiple labels — Vize app allows you to detect unlimited number of labels. The more labels you add, the more complex and difficult the recognition gets.

Requirements

  • Hundreds of images for each label
  • Clearly defined set of labels
  • Active Ximilar account
  • Few minutes of your time

Common Benefits

  • Significant wokflow speed-up
  • Instant cost saving compared to manual work
  • Evaluation statistics/insights of your models
  • Fast and parallel predictions
  • High accuracy for low number of labels

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.

see possibilities

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

Technology Stack

We use state of the art neural network models & machine learning techniques. Constantly trying to improve the technology so you always have the best quality available. Each model has millions of parameters which can be processed by CPU or GPU. Our intelligent algorithm picks from several models & uses the best performing.

  • Tensorflow-Logo
  • Docker-Logo
  • Nvidia-Logo
  • IbmCloud-Logo
  • Python-Logo

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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