Overwatch 2 Players Detector
Supported Labels
['Head']
ALL my models YOLO11, YOLOv10 & YOLOv9
- YOLO11l: https://huggingface.co./jparedesDS/fluorescent-penetrant-inspection
- YOLO11x: https://huggingface.co./jparedesDS/welding-defects-detection
- 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
- YOLO11m: https://huggingface.co./jparedesDS/valorant-yolo11m
- YOLO11l: https://huggingface.co./jparedesDS/deadlock-yolo11l
- YOLO11l: https://huggingface.co./jparedesDS/ow2-yolo11m
How to use
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\ow2-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, 20,030,803 parameters, 0 gradients, 67.6 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 2.69it/s]
all 273 133 0.742 0.563 0.66 0.317