DSConvNet: A Lightweight Architecture for Extracting Image Features From Depthwise Separable Convolution Network for Edge Devices
Bibliographic record
Abstract
This paper presents DSConvNet, a novel architecture based on depthwise separable convolutional blocks for efficient multi-class image classification. Despite progress in compact convolutional neural networks (CNNs), many existing models still impose high computational costs on resource‑constrained devices, limiting their real‑time applicability. To address this, DSConvNet employs four optimized DSConvNet blocks that reduce parameters, accelerate inference, and stabilize training. Each block combines a 2‑D convolution, depthwise convolution, batch normalization, and max‑pooling, forming an efficient yet expressive feature extractor. The resulting architecture achieves lower complexity, mitigates overfitting, and improves generalization. DSConvNet was evaluated on nine benchmark datasets GTSRB, BTSC, CIFAR‑10, CIFAR‑100, MNIST, Fashion‑MNIST, Imagewoof, Imagenette, and Caltech‑101 covering 100 object categories. Without GPU acceleration, the model attained 99.10% accuracy on GTSRB and 98.90% on BTSC, confirming its suitability for real‑time edge-based image classification under limited computational resources.
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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) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".