NAM-YOLOV7: An Improved YOLOv7 Based on Attention Model for Animal Death Detection
Bibliographic record
Abstract
Dead animals on the road are very harmful to public health.Because of offensive odours and the potential outspread of diseases, dead animals endanger public health.Existing methods focused on collisions between vehicles and animals on roads, number of animals, protection of crops from animals etc.To solve this issue, two main tasks listed below can be used: (1) Detecting dead animals on the highway and (2) Notification to the appropriate authorities.In this paper, we explore the viability of object detection methods to detect dead animals.We scrutinize and compare various versions of the "YOLO"(you only look once) in detecting dead animals.We compare the performance with the improved YOLOv7 model with the earlier versions when trained on the ADD (Animal Death detection) dataset and results show that improved YOLOv7 performs best when compared to the earlier YOLO models.
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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 | 0.000 |
| 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".