Start Using the “Instance Segmentation” Module

Please click here to download an image dataset of wooden blocks. In this section, we will use an Instance Segmentation module and train a model to segment different types of wooden blocks and export the corresponding labels.

1. Create a new project and add the instance segmentation module

   Click on New Project in the interface, name the project, and select a directory to save the project. Click on in the upper right corner of the Modules panel and add the Instance Segmentation module.

   

2. Import the image dataset of wooden blocks

   Decompress the downloaded dataset file. Click on the Import button in the upper left corner, select Folder, and import the image dataset. The wooden blocks in the images are of four different shapes and colors.

   

3. Select an ROI

   Click on the ROI Tool button and adjust the frame to select the bin containing wooden blocks in the image as an ROI, and click on Apply to save the settings. Setting the ROI can avoid interferences from the background and reduce processing time.

   

4. Create Labels

   Select Labeling and click on the button in the Classes panel to create labels based on the type or feature of different objects. In this example, the labels are named after the different shapes of the wooden blocks. You can also name the labels according to different colors.

   

5. Label images

   Right-click on the button and select a proper tool to label the image. In this example project, the contours of the wooden blocks need to be outlined for segmentation. In addition, please make sure that the different shapes of wooden blocks have been labeled correctly.

   

6. Split the dataset into the training set and validation set

   By default, 80% of the images in the dataset will be split into the training set and the rest 20% will be split into the validation set. Please make sure that both the training set and validation set include objects of all classes to be segmented, which will guarantee that the algorithm can learn all different classes and validate the images properly.

   If the default training set and validation set cannot meet this requirement, please click on and drag the slider to adjust the proportion.

   You can also right-click the individual image and switch it to the training/validation set manually.

   

7. Train the model

   Keep the default training parameter settings and click on Train to start training the model.

   

8. Validate the model

   After the training is completed, click on Validate to validate the model and check the results.

   

9. Export the model

   Click on Export and select a directory to save the exported model (with file extension dlkpack). You can deploy the model according to actual needs.