Model Training

It refers to how much light is present in the image. When the on-site lighting changes greatly, by adjusting the brightness range, you can augment the data to have larger variations in brightness.

Contradiction in luminance or color. When the objects are not obviously distinct from the background, you can adjust the contrast to make the object features more obvious.

Add the specified horizontal and vertical offsets to all pixel coordinates of the image. When the positions of on-site objects (such as bins and pallets) move in a large range, by adjusting the translation range, you can augment the data in terms of object positions in images.

Rotate an image by a certain angle around a certain point to form a new image. In general, keeping the default parameters can meet the requirements. When the object orientations vary greatly, by adjusting the rotation range, you can augment the image data to have larger variations in object orientations.

Shrink or enlarge an image by a certain scale. When object distances from the camera vary greatly, by adjusting the scale range parameter, you can augment the data to have larger variations in object proportions in the images.

Flips the image 180° left to right. If the objects to be recognized have left-right symmetry, you can select the Flip horizontally check box.

Flips the image 180° upside down. If the objects to be recognized have vertical symmetry, you can select the Flip vertically check box.

Only supported in the Defect Segmentation module. Expand the regions of defects selected in an image by a certain scale. For most scenarios, you do not need to check this option. If the regions of defects are too small, you can select the Label expansion checkbox to avoid influence on training when the defect regions are too small after image scaling.

The pixel-wise height and width of the image input to the neural network for training. It is recommended to use the default setting, but if the objects or defect regions in the images are small, you need to increase the input image size. The larger the image size, the higher the model accuracy, but the lower the training speed.

The number of samples selected for each time of neural network training. It is recommended to use the default settings; if you need to increase the training speed, you can appropriately increase the batch size. If the batch size is set too large, memory usage will increase.

The number of epochs for each evaluation interval during model training. It is recommended to use the default setting. Increasing the Eval. interval can increase the training speed. The larger the parameter, the faster the training; the smaller the parameter, the slower the training, but a smaller value helps select the optimal model.

The total number of epochs of model training. It is recommended to use the default setting. If the features of objects to be recognized are complex, it is necessary to increase the number of training epochs appropriately to improve the model performance, but increasing the number of epochs will lead to longer training time.

The learning rate sets the step length for each iteration of optimization during neural network training. It is recommended to use the default setting. When the loss curve shows a slow convergence, you can appropriately increase the learning rate; if the accuracy fluctuates greatly, you can appropriately decrease the learning rate.

Graphics card information of the model deployment device. If multiple GPUs are available on the model deployment device, the training can be performed on a specified GPU.

This option is used to simplify the neural network structure. Unselected by default. When the training data is relatively simple, checking the option can improve the training and inference speeds.

Unselected by default. This option is to limit the maximum number of objects that can be recognized during training. In general, setting this parameter can speed up inference. Under certain circumstances, setting this parameter can help reduce GPU usage.