Cross temporal scale pig face recognition based on deep learning
Bibliographic record
Abstract
: Pig face recognition is a promising, non-invasive, and cost-effective method for monitoring pigs. However, rapid growth alters their appearance, challenging facial recognition. To address this, we build a cross-time-scale dataset with 33,434 images of 137 Bamaxiang pigs (26–76 days old). To validate the effectiveness of this dataset, we train and evaluat five CNN models on it. Specifically, ConvNeXt, ResNet, GoogLeNet, VGG, and MobileNet achieve accuracies of 98.6%, 97.2%, 96.6%, 93.9%, and 65%, respectively. Temporal scalability analysis show accuracy dropping from 97% to 69% as the training-test gap increased from 0 to 24 days. Besides, ResNet18 with a triplet loss function reaches 88.5% accuracy. While pig face recognition performs well, it remains highly sensitive to short-term facial changes, requiring future models to capture temporal features.
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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 | 0.001 | 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 | 0.001 |
| 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".