Car damage assessment

How to solve a classification problem

Zero prior AI knowledge is required. We’re going to plainly rip the image into a long list of numbers and put it through a deep neural network to find out what’s in that image. This is the simplest possible (and surprisingly powerful) starting point to deep learning, in our opinion.

You will do what your insurance company does, build a model that classifies damages.

Person - Target audience: Beginners
Spaceship - Tutorial type: Get started tutorial
Bell - Problem type: Tabular classification

You will learn to
Peltarion logo - Build a new AI model quick and easy.
Peltarion logo - Use a pretrained model in a classification problem.

Create project

First, click New project to create a project. Name it, so you know what kind of project it is. Naming is important.

New project button

A project combines all of the steps in solving a problem, from preprocessing of datasets to model building, evaluation, and deployment. Using projects makes it easy to collaborate with others.

Add dataset to the project

After creating the project, you will be taken to the Datasets view, where you can import data.

Click the Import free datasets button.

Import free datasets button

Look for the Car damage - tutorial data dataset in the list. Click on it to get more information.

If you agree with the license, click Accept and import.

Accept and import button

This will import the dataset in your project, and you can now edit it.

The car damage dataset

The car damage dataset contains approximately 1,500 unique RGB images with the dimensions 224 x 224 pixels, and is split into a training- and a validation subset.


The illustration show sample images from the various classes in the dataset. Note that the unknown class contains images of cars that are in either pristine or wrecked condition.

Each collected image represents one car with one specific type of damage. This means that the dataset can be used to solve a single-label classification problem.

Example images from each class
Figure 1. Example images from each class; Broken headlamp, Broken tail lamp, Glass shatter, Door scratch, Door dent, Bumper dent, Bumper scratch, Unknown

Build a model in the Experiment wizard

Click Use in new experiment to open the Experiment wizard.

Use in new experiment button

Name the experiment in the Experiment wizard.

  • Dataset tab
    Make sure that the Car Damage dataset is selected.

  • Inputs / target tab
    Select image as Input feature and class as Target feature.

  • Problem type tab
    Select the Single-label image classification.
    Single-label image classification is when a deep learning model predicts one class for each example.

Click Create, and all blocks needed will be added to the Modeling canvas.

Create button

Run experiment

The experiment is done and ready to be trained, so just click Run.

Run button

Analyze the experiment

In the Evaluation view, you can see how the loss gets lower for each epoch (when the complete training set has run through the model one time).

Loss graph

Model evaluation view — Training overview
Figure 2. Model evaluation view — Training overview

The loss indicates the magnitude of error your model made on its prediction. It’s a method of evaluating how well your algorithm models your dataset.

If your predictions are totally off, your loss function will output a higher number. If they’re pretty good, it’ll output a lower one. Is the loss low enough?

Yes, this is good to go.

Deploy your trained experiment

In the Evaluation view, click Create deployment.

Create deployment button

Select experiment and checkpoint.

Make the deployment public

Toggle the switch to make your deployment public. This will allow you to share your results with friends and colleagues on, e.g., with the link, Twitter, LinkedIn.

Private to public

Enable deployment

Click Enable to deploy the experiment. You can now call your deployed experiment with your own product using the Url and Token. For example, when you build your own app with Bubble.

Enable button

Test with deployment web app

We’ve also made it super easy for you to test the deployment.

Click on Open web app, and you will be directed to the Deployment web app.

Now, you just need an image to test. Try to download this image of a seriously damaged car or take your own photo.

Damaged car
Figure 3. Seriously damaged window. Photo by Prateek Katyal on Pexels.

Add the image to the app and click Get your result.

Result? Yes, the model prediction is glass_shatter.

Tutorial recap

In this tutorial, you’ve created an AI experiment that you trained, evaluated, and deployed. You have used all the tools you need to go from data to production — easily and quickly.

Next steps

Learn more about image classification

The tutorial Self sorting wardrobe will go into the details of how to build a deep learning experiment on the Peltarion Platform. Showing you why you take different steps on your journey to solving your problem. You will learn how to understand the building blocks and settings of a deep learning model.

Get started with tabular regression - price prediction

The tutorial Sales forecasting with spreadsheet integration shows you how to build a model that predicts daily sales revenue from many parameters. The platform lets you deploy this model for production, allowing you to directly integrate predictions in your Google Sheets or integrate predictions in your Microsoft Excel spreadsheets.

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