Guess the Shape


guess the shape


Create an AI model that detects geometric shapes

How to solve a classification problem

Artificial Intelligence can identify geometric shapes by practicing the classification of shapes according to the characteristics of each shape.


The problem

Classification problems have always been around. Humans have always tried to classify the world around them. Thanks to AI, this has now become much easier. With increased computing power, big data, and deep learning models we can now solve hard classification much faster and more accurately.

We will build an AI model that can guess shapes from real life using a webcam after training it on a dataset of geometric shapes


Classification is a supervised learning technique in which the computer uses input data (in this project, geometric shapes images are the input data) to learn and then classify new observations.


You will learn to

After completing this project, you will understand how to:

  • Collect data and split it into training and testing datasets. 
  • Build an AI model quickly and easily
  • Solve a classification problem. Create a model that can guess the geometric shape of real objects.
  • After training the model, you can deploy your experiment and start to use your AI application straight away.



The data

The dataset is comprised of images of geometric shapes provided as a subset of photos from the dataset library. This dataset will be used to train your ML model on how to do the classification task on its own when it sees newgeometric shapes images. You will need to make sure that your split your data into training dataset and testing dataset so that you know how to assess the performance of your ML model. You will use a ready data sets of geometric shapes for four types triangle , circle , square, star and an arrwo. These shapes were classified for you into 4 classes (This is Star shape - This is Circle shape -This is Square shape - This is Triangle  shape)


project steps: 


Meet your AI Identity. You will teach your AI how to complete tasks as a human would. In due time, your AI Identity will grow more intelligent as you train it some more to take on different tasks with multiple projects. Eventually -in the future- you will be able to transfer your AI Identity's intellect into a physical robot you can interact with in the real world.  



First, create a project and name it so you know what kind of project it is. Naming is important. A project combines all of the steps in solving a problem, from the pre-processing of datasets to model building, evaluation, and deployment. Using projects makes it easy to collaborate with others. After adding the project’s name and description, click on CREATE



In this step, you will need to select one of the ML models in order to solve your problem, that is classifying images. You will find a computer vision model (Make Me See), Natural Language Processing model (Make Me Read), Voice Recognition (Make Me Hear), and a tabular model (Make Me Count). In our case, you will need to train your AI transformer to how to see and classify geometric shapes images, therefore, you need to select MAKE ME SEE. 

More AI capabilities (models) will be added to the platform, so stay tuned! These models will help you to accelerate the process of creating a trained AI model that solves your problem, and thus export into your app to start using it!



This is the most important step in any ML project. In fact, data preprocessing is 70-80% of any ML project. In this section, you will need to manually collect yourgeometric shapes images from reliable resources, such as Kaggle, or import geometric shapes dataset from our data library. Four datasets classes were collected for you, the first class is This is Star shape , the second one is This is Circle shape, the third one is This is Square shape and the fourth is This is Triangle shape. Each class contains a set of shapes related to the class name. What you need now to click on TRAIN ME, thus AI model will train at these sets of images



Right now, and after you completed training your model, you are ready to test it using a new sample image. You can test your model using one of shapes images from the testing dataset (see step 4).

Just make sure that you don’t use an image from the dataset that you used in the training part, otherwise, you are cheating! Upload or import the sample image to assess the performance in the preview section. If you like the confidence score, you can export your model. Otherwise, go back to the "collect data" step and check whether you have used sufficient number of images in the training dataset. 

Click TRAIN ME to start the training. This may take a few moments.



If you are happy with the testing results, you can "Export" your ML model using our API into your own websiteapplication or even to your robot or any machine! If you are not happy with the confidence score, you will need to check your training data classes again, and make sure that you have clean and sufficient number of examples for each data class. Keep in mind the concept of GIGO (garbage in, garbage out) which means that the quality of your ML model output is determined by the quality of your input, i.e. training dataset