Use the Unsupervised Segmentation Module

Create a new project and add the Unsupervised Segmentation module: Click New Project after you opened the software, name the project, and select a directory to save the project. Then, click in the upper-right corner and add the Unsupervised Segmentation module.

Import the image data of bottles: Unzip the downloaded data file. Click the Import/Export button in the upper left corner, select Import Folder, and import the image data.

Select an ROI: Click the ROI Tool button and adjust the frame to set an ROI that covers the target objects in the images. 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, and the ROI boundary should be as close to the outer contours of the object as possible.

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.

Label images: Use OK Label or NG Label on the toolbar to label images.

Train the model: 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 the training is completed, click Validate to validate the model and check the results.

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.

Export the model: Click Export and select a directory to save the trained model.