Instructions to use nexwayne/fursuit-yolo11m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use nexwayne/fursuit-yolo11m with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("nexwayne/fursuit-yolo11m") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Fursuit Head Detection (YOLO11m)
Custom object detection model based on YOLO11m trained specifically for detecting fursuit heads in images and video streams.
The dataset includes negative background samples (real animals, birds, etc.) to minimize false positives.
Model Performance
| Metric | Value |
|---|---|
| mAP@50 | 0.918 (91.8%) |
| mAP@50-95 | 0.656 (65.6%) |
| Precision | 0.940 (94.0%) |
| Recall | 0.845 (84.5%) |
| Optimal Conf Threshold | 0.49 |
Usage Examples
Option 1: Direct Download via Python
from ultralytics import YOLO
# Load weights directly from Hugging Face Hub
model = YOLO("https://huggingface.co/nexwayne/fursuit-yolo11m/resolve/main/best.pt")
# Perform inference with optimal confidence threshold
results = model.predict(
source="path/to/image.jpg",
conf=0.49,
save=True
)
# Show results
results[0].show()
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