Application of Lightweight CNN in Garbage Image Classification
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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