Car Recognition Model

Content list

  1. Description
    1. Currently recognizable models
    2. Models architecture
  2. Authentication
  3. Authorization and sample requests
    1. Sending a Base64 encoded image
    2. Sending a binary image
    3. Valid response


This article describes how to use our car predicting model via API. The model is trained to recognize the brand and the model of a car based on its photo. The dataset used to train it was enhanced during the process using various random techniques of rotating, skewing, and cropping of the images.

The model can be used via our API for various tasks like:

  • live car recognition using camera
  • pre-checking the image uploads in online car markets
  • connecting with a multi-object classifying net – additional details for cars
  • identification or suggestion based on very few details

Check out our simple visual demo at

Currently recognizable models

The list below contains all car models recognized by our NN.

Every entry is formatted in a following manner: cars-brand_cars-model.

The total number of currently recognizable models is 439.

Models architecture

The model is based on 6 Convolutional layers, 2 Dense layers interlaced with a single Dropout layer. The total amount of parameters is below 4 million.

The models loss and accuracy plot can be found here.


To be able to communicate with MLAPI service you need to have a user account created. The best way for this to happen is to contact us via

When you already got your credentials you can login in the following manner:


JavaScript, ES6+


The response from the server on positive authentication should look like this:

Authorization and sample requests

Every request you want to send to the server should have your TOKEN appended.

As we are using JWT for authentication and authorization the manner you should append the TOKEN is as follows:

Sending a Base64 encoded image


JavaScript, ES6+


Sending a binary image


JavaScript, ES6+


Valid response (The result structure will change in the next version)


Want to try it out without all the typing? Check out our simple visual demo at