Enhancing Thai Food Classification: A CNN-Based Approach with Transfer Learning
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
In this research paper, we delve into the classification of Thai cuisine images.Despite Thailand's renowned reputation for its multicultural culinary landscape, there is a noticeable gap in dedicated studies on Thai food classification.This paper seeks to fill that void by applying deep learning methodologies, specifically Convolutional Neural Networks (CNNs), to the identification of Thai cuisine.Thai cuisine, shaped by regional and intra-regional variations, serves as a powerful cultural representation for the nation.The study employs image recognition through CNN and integrates transfer learning to enhance classification performance.The collaborative learning process between CNN and transfer learning contributes to achieving a noteworthy accuracy rate of 84%.While previous research has often overlooked the specificity of Thai cuisine, our aim is to shed light on the potential of deep classification networks, offering an engaging illustration for both researchers and food enthusiasts alike contributing to the broader field of food image classification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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