Getting started with Computer Vision AI / ML — Tutorial Step 3 of 7: Upload to Google Cloud, Labelling and Vision Training
In this tutorial, we learn how to create a system to detect if a sink contains any dishes that need washing up. This is step 3 of 7. Start with Step 1 here, or view the full tutorial on Gravio.com.
Steps to get into Machine Learning and Computer Vision
- Collecting images of as many different situations as possible (Details)
- Setting up your Google Cloud Vision account (Details)
- Uploading and labeling those images via the Google Vision AI website, training the Model 👈 YOU ARE HERE
- Download the model from Google’s platform (Details)
- Set up Gravio Coordinator and connect HubKit to it (Details)
- Deploy the model to Gravio Coordinator and subsequently to HubKit (Details)
- Create Actions that are being triggered based on what the camera sees (Details)
By now, you should hopefully have loads of (variants of) images collected from your camera. Gravio will save the pictures in the /mediadata/ folder of your Gravio installation. Just click on the camera device and your images will be split into one folder per day.
Take as many different images as you can and upload them into a Google bucket. In our case here, we created a subfolder and added them there:
Once uploaded, you can click on the “Images” tab and start creating the labels for the items you would like to detect:
Google then also provides an online tool to assign labels to items in the images. Note, this is a very tedious and time consuming task, you may want to consider outsourcing this to companies that specialise in image tagging.
But if you like to do it yourself, this is how it could look like:
You have to do it for a few dozen if not hundreds of images, until Google has enough examples it can split it into three types of images:
The “Validation” and “Test” columns will automatically fill up after a while, just ensure that you have uploaded and tagged enough images. You may have to do it in multiple batches.
That’s it!
Once your images are uploaded and you have enough types of them, you can start the training process. This is where you will incur costs, but the initial free Google credits should be enough for you to give it a first try. Just click on “Start Training”.
It may take a while for the training to finish (multiple hours), and once the training is finished, it’s time for you to download the models Google has created.
Note: If you like to test the models first, you can deploy the model on Google temporarily, and test it by uploading a random test image. Google will then try to detect objects and you will see if it worked:
That’s it, you’re ready to download the model and use it in a device such as Gravio to detect the items.
Continue to the next Step: Download the model from Google’s platform
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