Use the Classification Module

Create a new project and add the Classification module: Open Mech-DLK, click New Project in the interface, name the project, and select a directory to save the project. In the main interface, click the + button under the Input module, and select the Classification module in the Add Module window.

Import image data: Import the acquired image data or decompress the data file provided in this article. You can use one of the following methods to import image data:

Create classes: Select Labeling and click the + New button to create classes based on the types and characteristics of different objects. In this example, the classes are named front and back to distinguish between the front and back sides of the condenser.

Label images: Classify the images with corresponding classes. You can select multiple images and label them together. Please make sure that you have labeled the images accurately. 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. Please make sure that both the training set and validation set include images in all different classes, which will guarantee that the model can learn all the different characteristics and validate the images of different classes properly. If the default training set and validation set cannot meet this requirement, please right-click the name of the image and then click Switch to training set or Switch to validation set to adjust the set to which the image belongs.

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 the stop icon 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.

Show class activation map: After the training is completed, click Generate beside CAM. After the generation process is finished, click the CAM icon to view the CAM. The class activation maps show the areas in the images that are paid attention to when training the model, and they help check the classification performance, thus providing references for optimizing the model.

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.

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.