Evaluation of Top Pretrained Models Using Transfer Learning on Banknote Dataset with Quality Parameter
Bibliographic record
Abstract
Building a machine learning (ml) model for fast and accurate banknote classification is an open challenging problem. Image classification problems can be addressed in two ways: by building own model from scratch or by using the transfer learning technique. Building your model from scratch is time-consuming and does not guarantee the best results in the stipulated time. Transfer learning, on the other hand, is a popular technique used by many researchers to deploy ml models in less time with higher accuracy. This paper presents the evaluation of the top five pre-trained convolution neural network (CNN) models. This research aims to evaluate performance and find out the best suitable model from the available list for banknote classification with quality parameters. The model training was done on dataset of Indian banknote which included images from 16 classes, split into 8 classes for clean banknotes and 8 classes of spoilt banknotes. While performing the evaluations, we also consider the performance of models without fine-tuning and after finetuning.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".