Using alwaysAI’s Model Training Tool to Build a License Plate Tracker

Using alwaysAI’s Model Training Tool to Build a License Plate Tracker

In this tutorial, we’ll cover how to create your own license plate tracker using the license plate detection model that was created using alwaysAI’s model training tool. If you want to read more about how the license plate detection model was built, read this blog. To read more about model training in general, you can read this model training overview article.

In this tutorial, we’ll cover how to create your own license plate tracker using the license plate detection model that was created using alwaysAI’s model training tool. If you want to read more about how the license plate detection model was built, read this blog. To read more about model training in general, you can read this model training overview article.

The model training tool will become available soon, however if you’re interested in training your own object detection model now, you can fill out this survey to participate in our model training beta program now. You can use a freely available dataset to train your own license plate detection model, found here, which is 185MB, and consists of 584 image/annotation pairs. You can then test out your new model using the example app we’ll build in this tutorial! The finished code from this tutorial, as well as test videos and dataset links, is available on GitHub.

To complete the tutorial, you must have:

  1. An alwaysAI account (it’s free!)
  2. alwaysAI set up on your machine (also free)
  3. A text editor such as sublime or an IDE such as PyCharm, both of which offer free versions, or whatever else you prefer to code in

This app includes a few useful features you can incorporate in your other projects, namely:

  • Using a correlation tracker to reduce overhead
  • Using multiple files as input to your app
  • Labeling individual detected objects for readability

Please see the alwaysAI blog for more background on computer visiondeveloping models, how to change models, and more.

Let’s get started!

We’ll break down this tutorial in two parts:

  1. Set up
  2. App.py

Set Up

In this tutorial, we’ll build the app from scratch. After you’ve signed up and logged in, go to https://alwaysai.co and navigate to your Dashboard. You can follow the steps outlined here to create a new project. For this app, you’ll want to choose ‘create a project from scratch’. Once your project is created on the Dashboard, scroll down to the ‘Models’ section and click the ‘+’ sign to add a model. Browse the model category to find “alwaysai/vehicle_license_mobilenet_ssd”, which will be in the ‘object detection’ models. Add this model to your project by clicking on it, and selecting ‘add to project’ and choosing your project from the drop down menu. Navigate back to your project Dashboard to get the configuration hash code and then finish configuring your project locally as described in the documentation page.

Finally, create a folder inside the directory that app.py is in named ‘video’. This is where we’ll store the input videos. You can find some sample videos in the GitHub repository, along with the completed code for this tutorial.

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