A Two-Stage Deep Learning Framework for Enhanced Waste Detection and Classification
Bibliographic record
Abstract
With the rapid modernization progress over the past decades, waste classification has become increasingly important as cities worldwide seek to implement more sustainable waste management practices. Traditional manual sorting methods are labor-intensive, prone to inaccuracies, and hard to scale, driving the need for automated, efficient solutions. Although deep learning techniques, recognized for their ability to process complex hierarchical data, have emerged as potential aids, their successful implementation is often hampered by the challenging task of gathering diverse, large-scale, high-quality waste image datasets, leading to possible overfitting and model bias. This study proposes an innovative, two-stage waste detection framework that first identifies the bounding box of waste items, then classifies them into one of six primary categories, effectively addressing the inherent issues with previous methodologies by optimally utilizing available data and reducing overfitting and bias. Trained and evaluated on the comprehensive TACO and WaRP waste datasets, our model has demonstrated superior performance relative to existing methods, underscoring its promise as a scalable, accurate, and efficient solution for waste classification, thus offering exciting prospects for further research and practical applications in sustainable waste management.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".