Contrastive Self-Supervised Learning for Packaging Artwork Layer Classification with Text and Image Features
Bibliographic record
Abstract
Creating product packaging relies heavily on digital artworks, typically in multi-layered PDFs.Each layer in these artworks represents critical elements, such as text, brand logos, nutrient panels, die-lines, dimensions, varnish areas, and graphics, essential for accurate and consistent printing.However, the absence of standardized layer organization poses significant challenges to maintaining the quality and consistency of these digital artworks.To address this, we introduce a self-supervised learning framework for automated packaging artwork layer classification.We pre-train our model using contrastive learning methods, specifically simple contrastive learning (SimCLR) and Momentum Contrast (MoCo), leveraging unlabelled data.By strategically managing augmentations during pretraining, our framework extracts discriminative features, including shape, colour, and text, mapping semantically similar features into a shared representation space.Notably, MoCo enhanced feature learning and generalization through increased negative sample diversity.Additionally, this paper proposes a multi-modal classification approach that integrates image encoder with text embeddings.Experimental results show that our multi-modal model achieves a 91% accuracy rate, outperforming traditional machine learning models by 2% average accuracy.These findings highlight the model's ability to enhance classification performance, particularly in classifying visually similar artwork layers.We demonstrate the effectiveness of multi-modal architectures incorporating text, alongside visual features, for improved classification accuracy.This pre-training approach, particularly when combined with text embeddings, significantly boosts classification accuracy to 97% for complex artworks, representing a 5-10% improvement over baseline models.Our proposed solution streamlines production workflows through faster and more accurate layer identification.Ultimately, our research offers a scalable pathway towards standardizing packaging artwork management, improving consistency, reducing errors, and enhancing overall printing process efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".