To begin, move the deep train function from the general toolbar onto the screen to initiate data labeling and training. Click on the load images button within the deep train tool settings and navigate to the directory containing the images to import and label images. Then click and hold the left mouse button to label the objects and assign names to the selected objects.
Click save GT to save a dot GT file in the same directory. Before starting the model training, click the gen setting button to access data augmentation features. Utilize four types of augmentations, namely rotation, contrast, noise, and blur to enrich the dataset.
To begin the model training, click on the training button located within the deep train tool. In the generate training files function, choose the models, batch size, and subdivisions. Click the generate button to generate data and save it in the directory.
After configuring all settings, click the train button to initiate the model training. The program will automatically execute and adjust the dataset weight while it assesses the training loss. Upon achieving optimal loss, click the export button and save the weight file in the directory.
To proceed with object detection model evaluation, select evaluate in the plugin toolbar and move the eval detect function onto the screen. Afterward, click on the setting button. Wait for the three functions, detection, evaluate, and plot to appear.
To start the model evaluation, click the load config button and import the trained wait file from the directory. To import test images from the image file directory, click the browse button. Then click on the load GT button to import the GT file.
Next, click the evaluation button to assess the detection model in the directory. Upon completion, the results will be automatically saved as a CSV file in the same directory, sorted by class name, containing key parameters, such as true positive, false positive, false negative, recall, and precision for each class. To generate the precision recall or PR curve, navigate to the plot function.
Click on browse to import the CSV files from the directory. Select the desired classes from the list and click the plot button to visualize the PR curve. Click on the save button to save an image with AUC values of the PR curve in the required image format at the selected directory.
To select the image classification model training function, navigate to the image toolbar, select deep classif, and move classif train onto the screen. To import images for training, click on the open folder button within the classif train tool settings and navigate to the directory containing the images. Before training, enrich the dataset by clicking on the augmentation button and applying techniques such as rotation, contrast, flipping, noise, and blur.
To initiate the model training process, click the gen train button within the classif train tool. Under gen train, select the desired models, batch size, and subdivisions. Assign a directory to save the generated data and click the generate button.
Once all configurations are complete, click on the start button. The program will run continuously, assessing the training loss, and adjusting the weight of the dataset as needed. Upon achieving the desired loss level, click the export button and store the weight file in the specified directory.
