Fortnite Object Detection
Detection of player in fornite with AI (Yolo11)
Supported Labels
['head', 'player']
ALL my models YOLO11, YOLOv10 & YOLOv9
- Yolov9c: https://huggingface.co/jparedesDS/cs2-yolov9c
- Yolov10s: https://huggingface.co/jparedesDS/cs2-yolov10s
- Yolov10m: https://huggingface.co/jparedesDS/cs2-yolov10m
- Yolov10b: https://huggingface.co/jparedesDS/cs2-yolov10b
- Yolov10b: https://huggingface.co/jparedesDS/valorant-yolov10b
- Yolo11x: https://huggingface.co/jparedesDS/welding-defects-detection
How to use
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\fortnite-yolo11m.pt')
# Run inference on 'image.png' with arguments
model.predict(
'image.png',
save=True,
device=0
)
Confusion matrix normalized
Labels
Results
Predict
YOLO11m summary (fused): 303 layers, 20,031,574 parameters, 0 gradients, 67.7 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|ββββββββββ| 12/12 [00:06<00:00, 1.90it/s]
all 1014 2097 0.933 0.739 0.828 0.668
head 223 633 0.947 0.738 0.825 0.664
player 990 1464 0.919 0.74 0.831 0.673