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
Note :
- The object folder's name cannot exceed 30 characters in length.
- If you've uploaded images from your folder, the folder name will be used as the object name. Make sure to give a proper name to your folder before uploading.
After one or more images are uploaded, click on any image to view its preview. You can Zoom in, Zoom out, Fit to width, Fit to page, move to the previous and to the next image from the preview popup.
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 (trained, 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 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.
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:
- Deleting a model that is deployed in any of your applications will remove its deployment in those applications. This action cannot be undone.
- After deletion, the added fields (model input and output fields*link to use model*) will remain in the form in which the respective model is deployed. All the past data from the object detection model will remain as long as the respective fields are not deleted from the form.
- You cannot delete a model's version that is being current used. Instead, you can switch versions and then delete that model version.
After you train and test your model, you can publish it to make it available to your users and start detecting objects.
Note:
- Currently, you can add only image field as the source field. Therefore, only image type fields available in your form will be listed for source field selection.
- If there is no image field available in the chosen form, you will need to first create one in order to deploy the object detection model.
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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