Bronze Inscription Recognition Method Based on Automatic Pruning Strategy

Guoqiang Xia; Zhenhong Shang

doi:10.3788/LOP57.161025

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 16 >Page 161025 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 16, 161025 (2020)

Bronze Inscription Recognition Method Based on Automatic Pruning Strategy

Guoqiang Xia and Zhenhong Shang^*

Author Affiliations

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, China

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DOI: 10.3788/LOP57.161025 Cite this Article Set citation alerts

Guoqiang Xia, Zhenhong Shang. Bronze Inscription Recognition Method Based on Automatic Pruning Strategy[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161025 Copy Citation Text

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Fig. 1. Example of bronze inscription data set

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Fig. 2. Training loss and accuracy curves on training and test data sets. (a) Accuracy curve; (b) loss curve

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Fig. 3. L1 norm distributions of convolution kernes in C1,C2,C3 convolution layers . (a) C1 convolution layer; (b) C2 convolution layer; (c) C3 convolution layer

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Fig. 4. Final model retraining process and L1 norm distributions in convolution layers. (a) Retraining process; (b) L1 norm distributions

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Fig. 5. L1 norm distributions of convolution kernels in VGG16 convolution layer. (a) Before pruning; (b) after downsizing; (c) after pruning

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Fig. 6. L1 norm distributions of convolution kernels in ResNet18 convolution layer. (a) Before pruning; (b) after pruning

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Layer	Input size	Output size	Number of kernels	Number of parameters	FLOPS
C1	32×32×1	30×30×6	6	60	54000
M1	30×30×6	15×15×6			5400
C2	15×15×6	11×11×16	16	2416	292336
M2	11×11×16	5×5×16			1936
C3	5×5×16	1×1×32	32	12832	12832
FC	32	106		3498	3392
Total				18806	369896

Table 1. Network structure of LeNet

Number of iterations	C'₁	C'₂	C'₃	Number of parameters	FLOPS	Accuracy /%	Accuracy after retraining /%
0	6	16	32	18806	369896	100
1	6	13	15	8609	298003	82.36	100
2	6	13	14	8177	297571	68.81	100
3	6	13	13	7745	297139	62.93	100
4	6	13	12	7313	296707	67.62	100
5	6	13	11	6881	296275	65.79	99.77
6	6	13	10	6449	295843	59.52	98.71
7	6	12	10	6048	277322	51.85	98.04
8	6	11	10	5647	258801	43.46	97.62
9	6	11	9	5265	258419	39.9	89.17

Table 2. Iterative process of pruning and retraining

Network	Number of kernels	FLOPS /10⁶	Number of parameters /10³
VGG16	64(C1),64(C2),128(C3),128(C4),256(C5),256(C6),256(C7), 512(C8),512(C9),512(C10),512(C11),512(C12),512(C13)	333.19	34031
ResNet18	64(C1),64(C2),64(C3),64(C4),64(C5),128(C6),128(C7),128(C8),128(C9), 256(C10),256(C11),256(C12),256(C13),512(C14),512(C15),512(C16),512(C17)	37.22	11230

Table 3. Convolution layer structures of VGG16 and ResNet18

Network	Number ofiterations	Number of kernels after pruning	FLOPS /10⁶	Number ofparameters /10³	Accuracy /%
VGG16	18	39(C1),27(C2),86(C3),62(C4),93(C5),156(C6),0(C7), 176(C8),0(C9),0(C10),0(C11),0(C12),0(C13)	62.29	18450	77.96
ResNet18	39	64(C1),22(C2),64(C3),11(C4),64(C5),22(C6),128(C7),22(C8),128(C9),51(C10),256(C11),49(C12),256(C13),50(C14),512(C15),47(C16),512(C17)	9.22	1549	77.86

Table 4. Pruning results of VGG16 and ResNet18

Guoqiang Xia, Zhenhong Shang. Bronze Inscription Recognition Method Based on Automatic Pruning Strategy[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161025

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