Pig Face Recognition Based on Trapezoid Normalized Pixel Difference Feature and Trimmed Mean Attention Mechanism
Bibliographic record
Abstract
Pig face recognition has a wide range of applications in breeding farms, including precision feeding and disease surveillance. This article proposes a method to guarantee its performance in complex environments such as with dirty faces and in unconstrained outdoor conditions. First, inspired by the shape of the pig face, a trapezoid normalized pixel difference (T-NPD) feature is designed to achieve more accurate detection in unconstrained outdoor conditions. Subsequently, a trimmed mean attention mechanism (TMAM) uses the trimmed mean-based squeeze method to assign more precise weights to feature channels, and then fuses it into a 50-layer ResNet (ResNet50) backbone network to classify detected pig face images with high accuracy. In addition, the TMAM can be applied in numerous common networks due to its universality. Finally, comprehensive experiments conducted on the publicly available JD pig face dataset indicate that the proposed method has superior performance compared with other methods, with an overall accuracy of 95.06%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".