Generic Tagging

Quick deployment with a pre-trained tagging model with nearly 1,000 tags from areas such as animals, landscapes, cities & objects like person, weapon, camera or luggage.
  • Recognize
  • General Photos
  • Ready to use

Highly Probable

outdoor 99.66 %
river 97.25 %
nobody 91.50 %
bridge 88.55 %
house 77.28 %
tree 75.65 %
clouds 52.36 %

Related

town 49.78 %
sky 46.94 %
panorama 36.02 %

Try it yourself: See demo

Instant Launch

Critical benefit of general tagging is that you do not need to train your own model or upload any data. Just sign-up for Ximilar & use our API to get predictions immeditelly. We can run this in the cloud as well as on your own server if you do not want your data leave your machine.

Expanding the Tags

The set of tags is a base for any quick and simple AI system. But if a specific model requires tags special to your own use case, feel free to contact us, we can expand it and tune to your exact needs. Ximilar can expand your stock photo database with new knowledge and create better search experience.

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

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/tagging/generic/v2/tags' 
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 = { 
    '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/tagging/generic/v2/tags");

$data = [
    '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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