Created By: Adam Milton-Barker Public Project Link: https://studio.edgeimpulse.com/public/176144/latest

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Introduction

Even with the current limitations of Artificial Intelligence, it is still a very useful tool, and many tasks can be automated with the technology. As more tasks become automated, human resources are freed up, allowing them to spend more time focusing on what really matters to businesses: their customers. The retail industry is a prime example of an industry that can be automated through the use of Artificial Intelligence, the Internet of Things, and Robotics.

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Solution

Computer Vision is a very popular field of Artificial Intelligence, with many possible applications. This project is a proof of concept that shows how Computer Vision can be used to create an automated checkout process using the NVIDIA Jetson Nano and the Edge Impulse platform.

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Hardware

  • NVIDIA Jetson Nano Buy
  • USB webcam

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Platform

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Project Setup

Head over to Edge Impulse and create your account or login. Once logged in you will be taken to the project selection/creation page.

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Create New Project

Your first step is to create a new project.

Create Edge Impulse project

Enter a project name, select Developer or Enterprise, and click Create new project.

Choose project type

We are going to be creating a computer vision project, so now we need to select Images as the project type.

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Connect Your Device

Connect device

You need to install the required dependencies that will allow you to connect your device to the Edge Impulse platform. This process is documented on the Edge Impulse Website and includes:
  • Running the Edge Impulse NVIDIA Jetson Nano setup script
  • Connecting your device to the Edge Impulse platform
Once the firmware has been installed enter the following command: 
edge-impulse-linux
If you are already connected to an Edge Impulse project, use the following command: 
edge-impulse-linux --clean
Follow the instructions to log in to your Edge Impulse account.

Device connected to Edge Impulse

Once complete, head over to the Devices tab of your project and you should see the connected device.

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Dataset

Download data

We are going to use the Fruit and Vegetables SSM. This dataset has 18,000 images of various fruits and vegetables. In this example we will use images from the Apples, Bananas, Chillis and Broccoli classes.

Upload data

Once downloaded, unzip the data and navigate to the Train folder. Then proceed to upload the contents of Train/Apples, Train/Bananas, Train/Chillis and Train/Brocolli. Make sure to select Automatically split between training and testing, and enter the correct label.

Uploaded data

Once you have completed these steps, you should be able to see and filter your uploaded dataset.

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Create Impulse

Now we are going to create our neural network and train our model.

Add processing block

Head to the Create Impulse tab. Next click Add processing block and select Image.

Created Impulse

Now click Add learning block and select Transfer Learning (Images). Now click Save impulse.

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Transfer Learning

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Parameters

Parameters

Head over to the Image tab and click on the Save parameters button to save the parameters.

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Generate Features

Generate Features

If you are not automatically redirected to the Generate features tab, click on the Image tab and then click on Generate features and finally click on the Generate features button.

Generated Features

Your data should be nicely clustered and there should be as little mixing of the classes as possible. You should inspect the clusters and look for any data that is clustered incorrectly. If you find any data out of place, you can relabel or remove it. If you make any changes click Generate features again.

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Training

Training

Now we are going to train our model. Click on the Transfer Learning tab then click Auto-balance dataset, Data augmentation and then Start training.

Training complete

Once training has completed, you will see the results displayed at the bottom of the page. Here we see that we have 96.1% accuracy. Lets test our model and see how it works on our test data.

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Testing

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Platform Testing

Head over to the Model testing tab where you will see all of the unseen test data available. Click on the Classify all and sit back as we test our model.

Test model results

You will see the output of the testing in the output window, and once testing is complete you will see the results. In our case we can see that we have achieved 91.46% accuracy on the unseen data.

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On Device Testing

Before we deploy the software to the NVIDIA Jetson Nano, lets test using the Edge Impulse platform whilst connected to the Jetson Nano. For this to work make sure your device is currently connected and that your webcam is attached.

Live testing: Apple


Live testing: Banana


Live testing: Broccoli


Live testing: Chilli

Use the Live classification feature to record some samples for classification from the webcam connected to the Jetson Nano. Your model should be able to correctly identify the class for each sample. If you are not getting accurate detections, you may need to provide more data samples, or fine tune the training parameters to increase your accuracy. First, you will want to save a Version though.

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Versioning

Versioning

We can use the versioning feature to save a copy of the existing network. To do so head over to the Versioning tab and click on the Create first version button.

Versions

This will create a snapshot of your existing model that we can come back to at any time.

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Deployment

Now we will deploy the software directly to the NVIDIA Jetson Nano. To do this simply head to the terminal on your Jetson Nano, and enter: 
edge-impulse-linux-runner
This will then download the built model from Edge Impulse and start local inferencing on the Nano. Keep an eye out for a message that gives you a URL to view the results in your browser.

Live Inferencing

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Conclusion

Here we have created a simple but effective solution for classifying various fruits and vegetables using computer vision powered on an NVIDIA Jetson Nano, using Edge Impulse. You can train a network with your own images, or build off the model and training data provided in this tutorial.