--- tags: - fortnite - object - detection - yolo --- # 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 ![confusion_matrix_normalized.png](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/B9zPmKLks9x92bQ71W4WN.png) #### Labels ![labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/CSgOxmA86zJ8Bav6ChqOK.jpeg) #### Results ![results.png](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/3oSidFJgQyJL0JZS0nbZW.png) #### Predict ![val_batch1_pred.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/PxU0FT1r34-cg98lLJUiw.jpeg) ![val_batch2_labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/Ltah8kdNiv6GE1knl0HWv.jpeg) ![val_batch2_pred.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/A84rA88ASXon1QL96xhOG.jpeg) ``` 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