AI Models have undergone a major revamp and is now rechristened as AI Modeler that lets you build, train, and publish models to be used across your apps. If you've created models prior to this revamp, click here to know more.
To set up an object detection model, follow these 4 steps:
Step 1: Create an object detection model
Step 3: Verify the model summary, train and test model
After adding the training data, you can review the model details, such as Model Name, Model Type, Model Size, and Total Size. If you need to make any modifications, you can go back to do so. Otherwise, you can proceed to train the model.
Before you can actually use your object detection model in your application, you have to train it to perform the way you want.
After the training is complete, the user can view the status of the model ( t rained, failed, and draft), the model type, the date it was created on and updated on, and other details as mentioned below.
Under this section, you can view the current version of your model and the names of the added objects.
In this section, you can view the number of versions the model has, what version the model is currently running on, model creation date, the count of objects and their images.
In this section, you can view the App Name , Form Name , and the Field Names in which the model is deployed in. You can also filter between different environments to check which environment a model is deployed in.
After training, you can test the model's reliability before deploying it in any of your applications. This ensures that the model identifies the test object correctly with a good/high confidence score .
After testing your model, you'll get the identified object's name along with a confidence score .
After you train your model, you need to publish it to make it available for deployment in your applications.
Note : You cannot un-publish the model once it has been published.You can still make changes to the model and train it again.
Retraining the model with the additional images and removing unfavorable images helps your model detect images more precisely. Reworking on the model's efficiency allows the model to be tuned specifically to your business perspective.
Note : We recommend that you periodically re-train your model. This helps in improving the object detection model's reliability and accuracy.
After you train and test your model, you can publish it to make it available to your users and start detecting objects.
You can now access your app in live and upload your image which needs to be detected in the source field. The object detection field will try to detect the image and the image name will be displayed in the model output field.
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