Garbage Classification with Hardswish and Efficient Channel Attention: A GhostNetV2-Based Approach
Bibliographic record
Abstract
Classification of garbage data is essential for effective waste management and to make the environment sustainable. A lightweight yet highly accurate deep learning model is used for garbage image classification with the enhanced GhostNetV2 architecture. The Efficient Channel Attention (ECA) module is integrated into the selected blocks of GhostNetV2 which improves feature recalibration without significant computational overhead and the network focus on the most discriminative patterns. Additionally, the traditional ReLU activations are replaced with the Hardswish functions to boost non-linearity and to maintain computational efficiency. The garbage classification dataset contains ten classes, including 19,762 images, which allow for the comparing process across various types of garbage. The proposed model achieves a superior performance and experimental results show that the GhostNetV2-ECA model outperforms the baseline GhostNetV2 in terms of accuracy by achieving 96 % on the test dataset. The proposed model is lightweight in nature which makes it highly suitable for the real-world deployment on mobile and edge devices for intelligent waste management systems.
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How this classification was reachedexpand
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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".