Mdpianimal Models
PyTorch (Ultralytics) detection checkpoints behind the paper. 81 checkpoints: 9 detectors (YOLOv5/v8/v11 at n/s/m) × 3 datasets × 3 random seeds.
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- Type
- Other
- Taille du fichier
- 5,04–49,7 MB
- Version
- vRevision 18fa9fe
- Mis à jour
- 21 sept. 2026
- Téléchargements
- 242
- Source
- Hugging Face
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PyTorch (Ultralytics) detection checkpoints behind the paper. 81 checkpoints: 9 detectors (YOLOv5/v8/v11 at n/s/m) × 3 datasets × 3 random seeds.
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1 sourceVérifié 9 sept.
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Extraits de sources
2 extraits--- license: agpl-3.0 tags: - object-detection - yolo - animal-individual-identification - ultralytics --- # Edge-Deployed YOLO for Individual Identification of Cattle, Goats, and Mugger Crocodiles PyTorch (Ultralytics) detection checkpoints behind the paper. **81 checkpoints**: 9 detectors (YOLOv5/v8/v11 at n/s/m) × 3 datasets × 3 random seeds. ## Layout ``` <dataset>/<model>/seed{0,1,2}.pt dataset in {cattle, goat, crocodile} ``` `yolov8m` is the headline detector reported in the main text; the full set is the Appendix scale-sweep matrix. ## Training From COCO-pretrained weights, 500 epochs (early-stop patience 100), 640×640, batch 16, AdamW (auto). Strengthened augmentation: mosaic 1.0, HSV-H/S/V 0.02/0.7/0.4, rotation 90°, translate 0.1, scale 0.5, shear 10, perspective 0.0005, flipud 0.5, fliplr 0.0, dropout 0.2. ## Results — mAP@[0.50:0.95] (3-seed mean±SD, held-out test set) | Model | Cattle | Goat | Crocodile | |---|---|---|---| | YOLOv5n | 89.12±0.17 | 79.72±0.77 | 69.87±1.29 | | YOLOv5s | 91.51±0.43 | 81.26±0.51 | 69.79±1.77 | | YOLOv5m | 92.28±0.49 | 83.54±0.09 | 70.58±2.00 | | YOLOv8n | 90.50±0.34 | 83.50±0.37 | 69.60±2.71 | | YOLOv8s | 92.62±0.14 | 83.68±0.73 | 73.25±3.44 | | YOLOv8m | 95.18±0.32 | 84.15±1.65 | 81.20±1.10 | | YOLO11n | 90.48±0.76 | 85.73±0.20 | 75.78±2.87 | | YOLO11s | 93.09±1.14 | 85.04±0.36 | 74.96±4.45 | | YOLO11m | 93.15±1.10 | 84.92±0.66 | 69.09±4.47 | ## Results — [email protected] (3-seed mean±SD) | Model | Cattle | Goat | Crocodile | |---|---|---|---| | YOLOv5n | 99.41±0.14 | 94.75±0.87 | 93.30±1.04 | | YOLOv5s | 99.49±0.01 | 94.78±0.97 | 91.39±1.67 | | YOLOv5m | 99.48±0.00 | 95.25±0.92 | 93.56±1.51 | | YOLOv8n | 99.50±0.00 | 94.35±0.18 | 93.15±1.42 | | YOLOv8s | 99.50±0.00 | 94.14±0.60 | 93.64±0.79 | | YOLOv8m | 99.50±0.00 | 95.97±0.17 | 92.72±1.49 | | YOLO11n | 99.50±0.00 | 95.14±0.82 | 93.35±0.46 | | YOLO11s | 99.50±0.00 | 94.91±0.86 | 93.59±0.20 | | YOLO11m | 99.46±0.05 | 94.73±1.23 | 93.27±0.29 | ## Usage ```python from ultralytics import YOLO m = YOLO("cattle/yolov8m/seed0.pt") m.predict("image.jpg") ``` Datasets (640-px) are in the companion dataset repo `KZHIwEI/mdpianimal`.
Source context: 242 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo KZHIwEI/mdpianimal-models
