To get started, we first need to connect
weHub with an AI Lab account.
To do this,
open the weHub settings and enter your
AI Lab credentials.
Once that’s done,
the AI Lab status of the device changes from “Not Connected”
to “Not Registered”.
By connecting the device
to our AI Lab account under the actions tab,
the AI Lab status updates to “Registered”.
With that,
the connection of the AI Loop is successfully established
and we can continue with the next step:
creating a dataset in the AI Lab.
To do this,
go to the AI Lab landing page
and click the “CREATE DATASET” button.
First, we need to give the dataset a name,
so we enter one.
Next, we can add classes –
as many as we like, but at least two are required.
Once that’s done, we can go into the dataset
and copy the dataset ID.
In the next step,
we set up the Module Image AI in
wenglor uniVision 3.
First, open the camera settings
and make sure the trigger mode is set to “On”
and the trigger source is set to “Software”.
Then, add the Module Image AI
from the toolbox to the project tree.
Link the input image and enter
the dataset ID from the dataset
we just created in the AI Lab.
When selecting the “Upload to weHub” checkbox,
we can define further settings,
for example, how many images we want to add
and whether these images should have tags.
Tags help us distinguish good images
from bad ones directly at the machine.
So first, we upload 50 good images
and assign them the tag “OK”.
Once everything is configured,
return to the camera settings and, under “Acquisition Control”,
switch the trigger mode back to “Off”
so that the next 50 images will be automatically uploaded
to the AI Lab at the acquisition frame rate that we’ve set.
When we return to the AI Lab,
we can see that our images are being uploaded automatically.
After uploading our first 50 good images,
the Module Image AI in uniVision 3 informs us
that we need to reset the counter
before uploading more.
Before doing that,
we adjust our setup to capture “NOK” images
and enter this as a tag in the tag field.
Once that’s prepared, we can reset the counter.
From then on, the next 50 images are captured
automatically and uploaded to weHub.
After all images from both classes –
okay and not okay – have been uploaded,
we want to label them in batches.
To do this, we use the filter options to select the tags
we previously defined in uniVision 3.
First, we filter for our “ok” tags.
Once the images are filtered,
we select all of them and assign the ground truth label “ok”.
Then, we repeat the same
process for the “nok” images,
assigning them the corresponding
ground truth label.
Once all images in our dataset
are labeled, we can start training our model.
To do this, click the “TRAIN MODEL” button.
On the model configuration page,
we can choose the target device for deployment,
either B60 or MVC.
We can also define the image size for training and select
whether the images should be rectangular
or square.
When satisfied with the configuration,
click “TRAIN MODEL”
and the training process begins.
When the model training is finished,
the status on the model page will indicate completion.
Next, return to the
Module Image AI in uniVision 3
and open the AI model dialog.
Here, select “Load Primary Model”.
The model we just created is automatically
downloaded from the AI Lab into uniVision 3.
In the dialog window, a short preview of the model
and some additional information is displayed.
In the software,
we immediately
see how the model’s predictions change depending
on which image we select.
Thank you for watching.