Fig. 1. Comparison of LN83 diffraction pattern before (a) and after (b) gray value equalization

Fig. 2. Diffraction pattern of protein crystal after gray value equalization

Fig. 3. LN83 diffraction pattern image enhancement results (a) Original image, (b) Flip left and right, (c) Rotate 90° counterclockwise, (d) Rotate 25° counterclockwise and move 10 pixels to the right, (e) Rotate 110° clockwise, move 5 pixels to the right and 5 pixels to the down, (f) Rotate 60° clockwise

Fig. 4. Flow chart of convolutional neural network for training and prediction

Fig. 5. Accuracy and operation rate of verification set and test set based on different networks(a) Verification set accuracy, (b) Test set accuracy, (c) Verification set running rate, (d) Test setverification set running rate

Fig. 6. t-SNE dimensionality reduction results of six convolutional neural networks (the circle is the "maybe " sample, the cross is the "Miss" sample, and the pentagram is the "hit" sample)(a) MobileNets, (b) ResNet, (c) Inception-v1, (d) Inception-v3, (e) Vgg16, (f) AlexNet

Fig. 7. Running rate of LN83 on GPU and CPU

Fig. 8. MobileNets hit /maybe (a) and miss sample (b) reliability distribution

Fig. 9. Sample selected by MobileNets (a) Hit, (b) Maybe, (c) Miss

Table 1. Experimental data

Table 2. Data classification

Table 3. Five convolutional neural networks

Table 4. Verification set and test set accuracy of each samples based on MobileNets

Table 5. Accuracy of verification set and test set using different networks based on LN83

Table 6. Accuracy of two classification based on Ln83 sample