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:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("nexwayne/fursuit-yolo11m", "<weights>.pt") model = YOLO(weights) 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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