jparedesDS/fortnite-yolo11m

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%|██████████| 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

Others models...

https://huggingface.co./jparedesDS/valorant-yolov10b