Cotton color grading detection based on improved MobileNetV4
Bibliographic record
Abstract
In response to the issues of traditional cotton color grading relying on subjective human visual inspection and unstable instrument testing, this paper proposes a cotton color grade detection method based on an improved MobileNetV4. A dataset of five cotton color grades, from Grade 1 to Grade 5, was constructed using a self-designed image acquisition device. At the same time, comparisons were made with the GhostNet, ShuffleNetV2, and MobileNetV2 models to predict cotton color grading. The results show that the improved MobileNetV4 achieves an accuracy rate of 93% on the test set, representing improvements of 3.91 percentage points, 5.51 percentage points, and 2.14 percentage points over the GhostNet, ShuffleNetV2, and MobileNetV2 models, respectively, demonstrating superior detection performance.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".