Lightweight patch-level attention for efficient pig behavior detection: A novel dataset and approach
Bibliographic record
Abstract
The pig farming industry plays a crucial role in food production system. Pig behavior detection facilitates timely anomaly detection, enhances farming efficiency, ensures animal welfare, and prevents diseases. Pig behavior detection involves the automatic recognition and classification of pig behaviors in farm images using computer vision and deep learning techniques. Currently, mainstream approaches for pig behavior detection utilize neural networks for image analysis and recognition, however, deep neural network architectures are inherently complex and computationally intensive, with potential for further accuracy enhancement. To address these challenges, we propose a lightweight pig behavior detection model that integrates prediction head optimization and GIoU for improved bounding box regression, while utilizing patch-level attention instead of global attention and employing depthwise separable convolutions in place of standard convolutions. These modifications facilitate efficient and accurate identification and classification of five common pig behaviors in farm environments. Experimental results demonstrate that the proposed method achieves enhanced performance and efficiency.
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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 | 0.001 | 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".