Visual Geometry Group Network for Flexible Printed Circuit Board Surface Defect Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have drawn huge interest in the field of surface defect classification. During the production of flexible printed circuit boards (FPCBs), only a limited number of images of surface defects can be obtained. FPCB surface defect datasets have small samples and severe imbalances, which can significantly affect defect classification accuracy. Hence, this article presented a lightweight visual geometry group (L-VGG), developed by modifying the classical VGG16 network structure. The L-VGG network was optimized using L2 regularization and sample weighting, which alleviated the over-fitting phenomenon caused by small samples and improved validation accuracy. In addition, the differences among the classification accuracies of different defect images caused by imbalanced datasets were significantly reduced. The training time of the proposed L-VGG network was equivalent to 83.84% and 91.94% compression of the traditional VGG16 and ResNet18 networks, respectively. The dataset augmentation with generated images further mitigates the overfitting phenomenon caused by the small sample problem to some extent, and finally achieves a validation accuracy of 94.20%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it