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Zhang et al. J Mater Inf 2024;4:34 https://dx.doi.org/10.20517/jmi.2024.64 Page 11 of 28

Table 3. Training parameter configuration of transfer learning model for welding unfused defect recognition with the strategy of
loading pre-trained weights followed by performing entire fine-tuning
Training parameters Parameters values
Image resize dimension 224 × 224
Batch 8 or 32
Learning rate 0.001
Epoch 5000
Loss function Softmax cross entropy
Optimizer Adam

Table 4. The accuracy precision and recall metrics of typical convolutional neural networks
Metrics Accuracy (%) Precision (%) Recall (%)
Random initialization-ResNet34 78.18 81.23 80.56
500 Epochs trained-ResNet34 93.31 96.76 91.39
5000 Epochs trained-ResNet34 93.79 96.52 92.5
MobileNetV2 81.05 76.03 97.78
ConvNeXt 99.52 100 99.17

Figure 6. Data augmentation methods. (A) Original image; (B) flipping; (C) rotation; (D) resizing; (E-H) adjustment of lightness,
saturation, contrast and color; (I) cropping.