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Upload yolo26s fine-tuned model (.pt, .onnx, model card)

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README.md ADDED
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+ # Model Card — Black-tailed Prairie Dog (*Cynomys ludovicianus*)
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+
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+ Single-class detection model for Black-tailed Prairie Dog, fine-tuned from the Ultralytics
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+ YOLO26s backbone (pretrained on COCO).
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+
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+ **Model file:** `yolo26s_finetuned_prairie_dog_by_J.Gong_uwyo_2026-05-28.pt`
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+
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+ ## Training Details
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+
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+ | Property | Value |
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+ |----------|-------|
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+ | Base model | yolo26s.pt (COCO pretrained, Ultralytics) |
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+ | Architecture | YOLO26s |
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+ | Input size | 640 × 640 |
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+ | Epochs | 150 |
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+ | Optimizer | MuSGD, lr=0.002, momentum=0.9 |
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+ | Augmentation | mosaic=1.0, degrees=10°, scale=0.5, fliplr=0.5, hsv_h/s/v |
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+ | Device | NVIDIA RTX 5000 Ada Generation (32 GB, CUDA 12.8) |
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+ | Training date | 2026-05-28 |
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+ | Author | Jian Gong, University of Wyoming |
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+
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+ ## Dataset
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+
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+ Images sourced from iNaturalist (research-grade observations).
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+ Bounding boxes generated by MegaDetector v5a (confidence ≥ 0.15).
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+ Split 80 / 10 / 10 train / val / test.
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+
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+ | Split | Images |
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+ |-------|-------:|
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+ | train | 224 |
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+ | val | 28 |
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+ | test | 29 |
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+
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+ ## Performance
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+
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+ Evaluated on the held-out validation set (best checkpoint).
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+
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+ | Metric | Value |
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+ |--------|------:|
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+ | mAP50 | 0.8355 |
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+ | mAP50-95 | 0.7023 |
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+
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+ ## Usage
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+
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+ ```python
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+ from ultralytics import YOLO
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+ model = YOLO("models/prairie_dog/yolo26s_finetuned_prairie_dog_by_J.Gong_uwyo_2026-05-28.pt")
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+ results = model.predict("image.jpg", conf=0.25)
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+ ```
yolo26s_finetuned_prairie_dog_by_J.Gong_uwyo_2026-05-28.onnx ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 38120077
yolo26s_finetuned_prairie_dog_by_J.Gong_uwyo_2026-05-28.pt ADDED
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