Simplified Research on Daily Item Image Classification Based on MobileNet
Why this work is in the frame
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Bibliographic record
Abstract
With the popularization of mobile devices, the demand for mobile-end image classification has been growing increasingly. Traditional deep learning models are difficult to operate efficiently on mobile devices due to their large number of parameters and complex computations. This study takes daily items as the research objects and adopts MobileNet, a lightweight convolutional neural network, to achieve fast classification suitable for mobile devices by simplifying the network structure and applying transfer learning. Experiments were conducted to compare the accuracy difference between transfer learning and training from scratch, and to analyze the impact of different learning rates and batch sizes on model performance. The results show that the MobileNet model based on transfer learning not only ensures the classification accuracy but also significantly reduces the computational cost. It has high practicality in entry-level daily item classification tasks and provides a simplified and feasible solution for mobile-end image classification applications.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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