Use the Instance Segmentation Module

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

Import the image data of wooden blocks: Unzip the downloaded data file. Click the Import/Export button in the upper left corner, select Import Folder, and import the image folder. The wooden blocks in the images are of four different shapes and colors.

Select an ROI: Click the ROI Tool button and adjust the frame to select the bin containing wooden blocks in the image as an ROI. Then, click the button in the lower right corner of the ROI to save the setting. Setting the ROI can avoid interferences from the background and reduce processing time.

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

Label images: Right-click the button and select a suitable 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. Click here to view how to use labeling tools.

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. You can click and drag the slider to adjust the proportion. Please make sure that both the training set and validation set include objects of all classes to be segmented. If not, select the image name and then right-click it for adjustment.

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

Monitor training progress through training information: On the Training tab, the training information panel allows you to view real-time model training details.

View training progress through the Training Chart window: Click the Show chart button under the Training tab to view real-time changes in the model’s accuracy and loss curves during training. An overall upward trend in the accuracy curve and a downward trend in the loss curve indicate that the current training is running properly.

Stop training early based on actual conditions (optional): When the model accuracy has met the requirements, you can save time by clicking the Training center button, selecting the project from the task list, and then clicking to stop training. You can also wait for the model training to complete and observe parameter values such as the highest accuracy to make a preliminary assessment of the model’s performance.

Validate the model: After training is completed or manually stopped, click the Validate button under the Validation tab to validate the model.

Check the model’s validation results in the training set: After validation is complete, you can view the validation result quantity statistics in the Validation statistics section under the Validation tab. Click the View full report button to open the Detailed Report window and view detailed validation statistics. The Statistical matrix in the report shows the correspondence between the validated data and labeled data of the model, allowing you to assess how well each class is matched by the model. In the matrix, the vertical axis represents labeled data, and the horizontal axis represents predicted results. Blue cells indicate matches between predictions and labels, while the other cells represent mismatches, which can provide insights for model optimization. Clicking a value in the matrix will automatically filter the image list in the main interface to display only the images corresponding to the selected value.

Restart training: After adding newly labeled images to the training set, click the Train button to restart training.

Recheck model validation results: After training is complete, click the Validate button again to validate the model and review the validation results on each dataset.

Fine-tune the model (optional): You can enable developer mode and turn on Finetune in the Training Parameter Settings dialog box. For more information, see Iterate a Model.

Continuously optimize the model: Repeat the above steps to continuously improve model performance until it meets the requirements.