• Object detection model is trained to identify defined objects from digital photographs with a certain accuracy level. Refer to the  Understand object detection model page to learn more.
• You can  build custom object detection models , suited to your business needs, or choose a  ready to use  model that  can be directly deployed in your applications. 

To set up an object detection model, follow these 4 steps:

Step 1: Create an object detection model

Step 2: Add training data

Step 3: Verify the model summary, train and test model

Step 4:  Publish and use model

Step 1: Create an object detection model

1. Navigate to  Microservices under the  DEVELOP section in your Creator  homepage . All your  microservices will be listed here.
   
   

2. To create an object detection model :

• If you're creating an AI model for the first time, click the  + Create New button in the center of the  Microservices page.
• If you've already created AI models, then click the  + Create New button in the top-right of the  Microservices page.

3. Click the  Create button  in the  AI Models card. The  AI Models home page contains two model types -  Custom models that can be built according to the needs of the user and  Ready-To-Use  models that can immediately be deployed into their applications. 
   Note : You can create both custom and ready-to-use object detection models. To learn more about the model types,  refer here  . 
   

4. Click the  Object Detection card under the  Build-Custom Models section in the  AI Modeler  page.
   
5. Enter a  Model Name and click  Create Model. The  Add Training Data screen will appear.
   Note: The model name cannot exceed  30 characters in length.  
   

1. Click  + Add New Object  to create an object folder. You can also import images from your .zip folder by clicking  Import from folder .
   

2. To  help you get started quickly and explore the possibilities of our object detection model, you can download our sample data and start building models.
   Note : This sample data will be available only when you're creating an object detection model for the first time.

3. Enter the  object's name in the  New Object pop up that appears, then click  Create.  This is the name of the folder where you can add your input images. This object name will be displayed when the model detects your object from the input image. 
   Note : 

• The object folder's name cannot exceed  30 characters in length.
• I f 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.
  

4. Double -click the newly-created object folder to add the sample images.

5. Click  Upload Images to add different images of the same object at varying angles.
   Note :
1. A  minimum of 10 images must be uploaded for each object.
   
2. Compatible image formats are JPG, PNG,  and TIF.
   
3. Each image can have a file size up to 5 MB . 
   
4. For more image guidelines, click  here .
   

6. Click  Add Images to upload more images. You can also upload multiple images at once by either dragging and dropping them or uploading them as a .zip folder.
   Compatible image formats for.the zip folder include JPG, PNG, TIF.  

7. You can either click the  Select All radio button or individually select images and click  Delete to remove the images.
   
8. Click  Done . An object folder will be created.
9. You can add additional objects (folders) by clicking  + Add New Object  or Import from folder in the dropdown beside it at the top-right corner of the   Add Training Data  screen .

10. Select a folder to  Rename or  Delete it. Click  Select All (ref above image) to  Rename or  Delete all object folders.
    
11. Click Next . The  Model Summary screen will open.
    

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. 

• Train model
• View and manage model details
• Test model

Train Model

Before you can actually use your object detection model in your application, you have to  train it to perform the way you want.

1. Check the details of your model in the   Model Summary page and make any necessary changes by going  Back . You can modify the Model Name, upload additional images or remove unwanted images.

2. Once you've made the necessary changes, click  Train Model .
   Note : Model training may take some time, so you can either stay on the page and wait, or you can close the page and come back later.

View and manage model details

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. 

• Model Details
• Version Details
• Model Deployment

Model Details

Under this section, you can view the current version of your model and the names of the added objects.

Version Details

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. 

Model Deployment

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.

Test Model

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 .

1. Click  Test Model in the top-right corner of the page that appears after the model is successfully trained. This allows you to test the model's accuracy before publishing it.

2. The  Test Model popup will appear. Upload an image of the object that you've added as training data. The model will try to detect the object and display its name on the right side of the popup under  Model Output .
   Note : If the model does not detect the test image, you can refer  here to improve your model performance.
   

After testing your model, you'll get the identified object's name along with a  confidence score .

• If your confidence score is  high, you can proceed to publish your model.
• If your confidence score  is good, you can re-train your model with  additional images .
• If your confidence score  is poor, you need to check for inconsistencies in your training data like insufficient images, make the necessary changes, and train your model again.

Manage Object Detection Model

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.

• Retrain model
• Publish model
• Use model

Retrain model 

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. 

1. Click the  three-dot ellipsis at the top-right corner of your page.
   
2. Click  Retrain to train your model again. A new version will be created and listed under  Version Details .
3. Click  Edit to make changes to your model. These changes include modifying your model name, object folder name, and adding/removing image. You need to  train your model again for these changes to be reflected.
4. Click  Rename to edit your model's name. A popup will appear, where you can edit the model's name and click  Rename .
5. Click  Delete to delete your model.
   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 ) 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.

Publish model

After you train and test your model, you can publish it to make it available to your users and start detecting objects.

1. Click  Publish Model in the top-right corner.
2. Click  Publish in the  Publish popup that appears on the screen.
   
   Note: Once a model has been published, you cannot un-publish the same.

Use Model

1. After the model has been published, you can click  Use Model either in the popup that appears or at the top-right corner of your page.
   
2. Select the  Application Name and  Form Name from the dropdown list in the  Use Model popup that appears, then click  Use Model .
   
3. The user will be redirected to the form builder of the application they've selected to have the model deployed in.
4. The  Object Detection popup will appear with the  Model Input  screen open. 
   Note: The trained and chosen model will already be selected in the  Select Model  section. You can choose a model when you create a r eady-to-use object detection mode l . 
   
5. In the  Model Input  section, select the source field from the drop down menu and click  NEXT . 
   Note : 
6. 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.
   
7. 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. 

6. By default, the model name will be displayed as the field name.You can edit the  Field name and choose a field type ( Single Line or  Multi Line ) for the  Model Output field. 
   Note : The field name cannot exceed  30 characters in length.

7. Click  ADD FIELD . A new field object detection field will be created.
   

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.

Related Topics

• Understand Object Detection Model
• Understand AI Models
• Custom AI Models
• Understand Object Detection Field