A Progressive Weighted Average Weight Optimisation Ensemble Technique for Fruit and Vegetable Classification
Bibliographic record
Abstract
Image classification of fruit and vegetables at supermarket self-checkouts is a complex problem. Significant variations in the size, shape and colour of objects are involved, along with potentially large variations in the environmental conditions, must be accommodated to implement such a robust and effective system. Convolution Neural Networks (CNNs) have shown promising results for object classifications. However, the scarcity of training datasets due to the diversity of varieties and applications of fruit and vegetable classification is a significant limitation to the CNN implementation for this task. To overcome this, we propose the use of transfer learning and ensemble technique. Specifically, a transfer learning based weighted average weight optimisation ensemble technique is applied to the weights of GoogleNet and MobileNet by transfer learning the pre-trained CNNs using a custom dataset. Two hyperparameter optimisation techniques have been applied in a sequential way to identify the effective weights progressively. The optimised weights are used as an input to a normalised exponential softmax layer to estimate the final probability distribution for classification. A comparative evaluation among standalone GoogleNet, MobileNet and different levels of ensemble has been presented, which supports the adoption of this technique as a solution in a real-world supermarket environment.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".