Custom Image Recognition API

Recognize and automate your images

Histological image
Normal cell
Histological image
Abnormal cell
Type of stripes
Horizontal
Type of stripes
Vertical
Type of stripes
Diagonal
Is there a swimming pool?
Yes
Is there a swimming pool?
No
Surveillance cameras
Empty
Surveillance cameras
Normal
Surveillance cameras
Suspicious
Home scene
Living room
Home scene
Bedroom
Home scene
Kitchen
Home scene
Bathroom
Explore use cases

Powerful artificial intelligence. Made easy.

High accuracy

Use deep learning algorithms with the highest accuracy on the market.

Simple setup and use

Implement cutting-edge vision automation faster with no development costs.

Recognize anything

Train custom neural network to recognize your specific images.

Training interface

Create powerful and custom image recognizers in intuitive web interface.

Scalable

Scale up easily with no infrastructure costs.

Always improving

We always improve the underlying machine learning algorithms so you are up-to-date.

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Intuitive interface

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

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Setup in 5 minutes

1

Define

Define your categories and upload sample images.

2

Train

Custom neural network is trained. Preview the results right away.

3

Recognize

Send us an image and recognize it with your custom neural network.

Use cases

Explore where custom image recognition API helps across industries.

Specific

E-commerce

Image analytics

IoT

Real estate

Industry

Developers first

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

see documentation

curl -v -XPOST -H 'Authorization: Token {__API_TOKEN__}' -F 'image_file=@'{__IMAGE_FILE__}';type=image/jpeg' -F 'task={__TASK_ID__}' https://api.vize.ai/v1/classify/import requests

url = 'https://api.vize.ai/v1/classify/'
headers = {'Authorization': "Token {__API_TOKEN__}"}
files = {'image_file': open('{__IMAGE_FILE__}', 'rb')}
data = {'task': '{__TASK_ID__}'}

response = requests.post(url, headers=headers, files=files, data=data)
if response.raise_for_status():
    print(response.text)
else:
    print('Error posting API: ' + response.text)

$curl_handle = curl_init("https://api.vize.ai/v1/classify/");

curl_setopt($curl_handle, CURLOPT_POST, 1);
$args['image_file'] = new CurlFile({path/myimage.png}, 'image/png');
$args['task'] = {__TASK_ID__};
curl_setopt($curl_handle, CURLOPT_POSTFIELDS, $args);
curl_setopt($curl_handle, CURLOPT_RETURNTRANSFER, true);
curl_setopt($curl_handle, CURLOPT_HTTPHEADER, array(
    "Authorization: Token {__API_TOKEN__}",
    "cache-control: no-cache",));

$returned_data = curl_exec($curl_handle);
curl_close ($curl_handle);
echo $returned_data;var unirest = require('unirest');

// Use http://unirest.io/nodejs open-source library. Install: npm install unirest

unirest.post("https://api.vize.ai/v1/classify/")
.header("Authorization", "Token {__API_TOKEN__}")

.header("Content-Type", "multipart/form-data")
.header("Accept", "text/plain")
.attach('image', 'cat2.jpg')
.attach('task', {__TASK_ID__})

.end(function (result) {
  console.log(result.status, result.headers, result.body);
});// Use http://unirest.io/java open-source library.

HttpResponse response = Unirest.post("https://api.vize.ai/v1/classify/?image={__IMAGE_FILE__}")
.header("Authorization", "Token {__API_TOKEN__}")
.header("Content-Type", "multipart/form-data")
.header("Accept", "text/plain")
.attach('task', {__TASK_ID__})
.asString();// Use http://unirest.io/objective-c open-source library.

NSDictionary *headers = @{@"Authorization": @"Token {__API_TOKEN__}", @"Content-Type": @"multipart/form-data", @"Accept": @"text/plain"};
UNIUrlConnection *asyncConnection = [[UNIRest post:^(UNISimpleRequest *request) {
  [request setUrl:@"https://api.vize.ai/v1/classify/?image={path/myimage.png}"];
  [request setHeaders:headers];
}] asundefinedAsync:^(UNIHTTPundefinedResponse *response, NSError *error) {
  NSInteger code = response.code;
  NSDictionary *responseHeaders = response.headers;
  UNIJsonNode *body = response.body;
  NSData *rawBody = response.rawBody;
}];// Use http://unirest.io/net open-source library.

Task> response = Unirest.post("https://api.vize.ai/v1/classify/?image={path/myimage.png}")
.header("Authorization", "Token {__API_TOKEN__}")
.header("Content-Type", "multipart/form-data")
.header("Accept", "text/plain")
.attach('task', {__TASK_ID__})
.asString();# Use http://unirest.io/ruby open-source library.

response = Unirest.post ("https://api.vize.ai/v1/classify/?image={path/myimage.png}",
  headers:{
    "Authorization" => "Token {__API_TOKEN__}",
    "Content-Type" => "multipart/form-data",
    "Accept" => "text/plain"
  },
  parameters:{
    "task" => "{__TASK_ID__}"
  })
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