metadata
language:
- en
- es
base_model:
- jameslahm/yolov10s
tags:
- GAMES
- COUNTER STRIKE2
- CS2
- VIDEOGAMES
- CS
- OBJECT DETECTION
- YOLO
- YOLOV10
- YOLOV10S
pipeline_tag: object-detection
Counter Strike 2 players detector
Supported Labels
['CT', 'CT_head', 'T', 'T_head']
ALL my models YOLOv10 & YOLOv9
- Yolov10s: https://huggingface.co/jparedesDS/cs2-yolov10s
- Yolov9c: https://huggingface.co/jparedesDS/cs2-yolov9c
- Yolov10m: https://huggingface.co/jparedesDS/cs2-yolov10m
- Yolov10b: https://huggingface.co/jparedesDS/cs2-yolov10b
How to use
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\yolov10s_cs2.pt')
# Run inference on 'image.png' with arguments
model.predict(
'image.png',
save=True,
device=0
)
Labels
Results
Predict
YOLOv10s summary (fused): 293 layers, 8,038,056 parameters, 0 gradients, 24.5 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 5/5 [00:03<00:00, 1.41it/s]
all 160 372 0.958 0.94 0.979 0.772
ct_body 88 110 0.964 0.964 0.988 0.861
ct_head 82 104 0.946 0.847 0.953 0.634
t_body 70 84 0.986 0.976 0.99 0.866
t_head 62 74 0.938 0.973 0.984 0.728