In order to compare the effect of the dataset under different conditions, the collected
images are divided into color dataset, gray dataset, and segmented dataset. The segmented dataset eliminates the influence of the background on the picture.
6.3 Analysis and Discussion
The results are shown in the line chart. It can be seen from the chart, under the same
condition, the average accuracy of the full-precision model is slightly higher than that
of the binarized model. Due to the high initial learning rate, the accuracy of the two
networks is improved rapidly before 20 epochs, and eventually, it tends to be stable.
It can be seen from the green polyline, because of the fine-tuning of the model the
initial accuracy of the full-precision model is higher. The color, segmentation, and
grayscale datasets, respectively, reach the accuracy of 0.6, 0.7, and 0.5. For the red
polyline, even if the parameter of trained model is used, the binarized parameter leads
to lower initial accuracy, but the accuracy tends to be stable after 30 epochs. Four
proportion datasets are set in this paper. It can be seen from the last line chart that the
green polyline and red polyline have a higher accuracy, followed by yellow and blue
polyline. It proves that the more the training sample, the more features can be extracted
to train networks (Fig. 2).
Fig. 2. Line chart of experimental results
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X. Pu et al.
images are divided into color dataset, gray dataset, and segmented dataset. The segmented dataset eliminates the influence of the background on the picture.
6.3 Analysis and Discussion
The results are shown in the line chart. It can be seen from the chart, under the same
condition, the average accuracy of the full-precision model is slightly higher than that
of the binarized model. Due to the high initial learning rate, the accuracy of the two
networks is improved rapidly before 20 epochs, and eventually, it tends to be stable.
It can be seen from the green polyline, because of the fine-tuning of the model the
initial accuracy of the full-precision model is higher. The color, segmentation, and
grayscale datasets, respectively, reach the accuracy of 0.6, 0.7, and 0.5. For the red
polyline, even if the parameter of trained model is used, the binarized parameter leads
to lower initial accuracy, but the accuracy tends to be stable after 30 epochs. Four
proportion datasets are set in this paper. It can be seen from the last line chart that the
green polyline and red polyline have a higher accuracy, followed by yellow and blue
polyline. It proves that the more the training sample, the more features can be extracted
to train networks (Fig. 2).
Fig. 2. Line chart of experimental results
16
X. Pu et al.