⑵ loss curve
A good model should not only have high accuracy but also good
generalization ability. Poor generalization ability of the model leads
to overfitting. The model’s mAP is plotted as shown in Figure 9(b). It
is proved that precision of the model has been improved.
The model parameters are adjusted to improve the model’s generalization
ability while improving the model’s accuracy. The model’s loss curves as
shown in Figure 9(a) are drawn. By visualizing the channel feature
graph, the content of each channel is represented by binary images to
judge the performance of the convolution kernel. The training process of
the model is divided into three stages, namely, the stage of substantial
decline, the stage of small decline and the stage of keeping
convergence. In the stage of substantial decline, the learning rate
drops by gradient, and then the loss curve changes to the stage of
slight decline. When learning reaches a certain stage, the loss curve
region is stable and begins to converge. Figure 9(a1) shows the loss
curve of Faster RCNN. The model is iterated for 150 times. As the number
of iterations increases, the loss curve decreases continuously until it
becomes stable and convergent. The feature maps of A1, B1 and C1
correspond to the stage of substantial decline, the stage of small
decline and the stage of keeping convergence respectively. Figure 9(a2)
shows the loss curves of the RetinaNet model with 300 iterations. A2, B2
and C2 feature maps correspond to the stage of substantial decline, the
stage of small decline and the stage of keeping convergence
respectively.
The improved RetinaNet model is used to predict the validation set of
the surface defect dataset. Precision and recall values are obtained.
P-R curve is drawn, as shown in Figure 10. The horizontal axis of the
P-R curve is recall and the vertical axis is precision. Under a certain
threshold value, the model determines that the results greater than the
threshold value are positive samples, and those less than the threshold
value are negative samples. The points on the curve represent the recall
rate and accuracy rate corresponding to the returned results. The origin
represents the precision and recall of the model when the threshold is
maximum. Models with high Precision and low Recall are not able to
complete some tasks requiring precise positioning. Models with low
Precision and high Recall are characterized by low model recognition
rate.