CS2 YOLO - Object Detection
Collection
4 items
β’
Updated
[ "none", "ct_body", "ct_head", "t_body", "t_head" ]
YOLOv10b
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\yolov10b_cs2.pt')
# Run inference on 'image.png' with arguments
model.predict(
'image.png',
save=True,
device=0
)
YOLOv10b summary (fused): 383 layers, 20,418,862 parameters, 0 gradients, 98.0 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|ββββββββββ| 3/3 [00:02<00:00, 1.44it/s]
all 90 215 0.908 0.757 0.85 0.635
ct_body 41 45 0.914 0.911 0.953 0.819
ct_head 43 47 0.909 0.639 0.735 0.465
t_body 54 60 0.932 0.921 0.964 0.781
t_head 56 63 0.877 0.556 0.748 0.476
Base model
onnx-community/yolov10b