Use deep learning algorithms with the highest accuracy on the market.
Implement cutting-edge vision automation faster with no development costs.
Train custom neural network to recognize your specific images.
Create powerful and custom image recognizers in intuitive web interface.
Scale up easily with no infrastructure costs.
We always improve the underlying machine learning algorithms so you are up-to-date.
User interface to create, manage and deploy your custom image recognition models. No machine learning or coding knowledge required.
Define your categories and upload sample images.
Custom neural network is trained. Preview the results right away.
Send us an image and recognize it with your custom neural network.
Explore where custom image recognition API helps across industries.
We take care of the complexity behind and wrap it in a few lines of code.
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 not 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__}" })