Waste Management Using Convolutional Neural Network and Object Detection
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
Effective waste management is a global challenge, and accurate waste classification is essential for improving recycling and disposal processes. Traditional models like YOLO and VGG often struggle with complex waste types and overlapping items. In this paper, we propose a Convolutional Neural Network (CNN)-based approach to address these limitations. The CNN model captures spatial features and structural relationships, enabling accurate classification of irregular and overlapping waste items. By utilizing transfer learning and data augmentation, our model achieved an accuracy of 96.6% after 20 epochs and 98.2% after 30 epochs. This method significantly enhances classification accuracy, data efficiency, and adaptability, making it a scalable and reliable solution for automated waste management, contributing to more sustainable practices.
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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 | 0.000 |
| 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