Application of Lightweight CNN in Garbage Image Classification
Bibliographic record
Abstract
With urbanization accelerating, domestic waste output has surged, and garbage classification is crucial for alleviating environmental pressure and recycling resources. Traditional manual classification is inefficient, costly, and subjective, so automated garbage classification technology is necessary. To solve problems of existing CNN models like large parameter size, slow inference, and difficulty in edge - device deployment, this paper proposes a lightweight CNN based on a simplified ResNet for garbage image classification. The public TrashNet dataset with 6 common domestic waste categories is used. Data augmentation and transfer learning are employed to optimize the model's adaptation to garbage image features. Experimental results show the simplified ResNet model achieves 91.2% classification accuracy on the TrashNet dataset, with precision of 90.8%, recall of 90.5%, and an F1 - score of 90.6%. Its parameter number is only 48% of the traditional ResNet18, and the per - image inference time is shortened to 12.3ms. Compared with mainstream models, it reduces computational complexity and storage requirements while ensuring performance, making it more suitable for edge - computing devices like smart trash cans and classification robots, and providing an efficient solution for practical automated garbage classification.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".