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---
language:
- en
- es
base_model:
- jameslahm/yolov10s
tags:
- GAMES
- COUNTER STRIKE2
- CS2
- VIDEOGAMES
- CS
- OBJECT DETECTION
- YOLO
- YOLOV10
- YOLOV10S
---
# Counter Strike 2 players detector
### Supported Labels
train: ../train/images
val: ../valid/images
test: ../test/images
nc: 4
names: ['CT', 'CT_head', 'T', 'T_head']
### models
YOLOv10s
## 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
![labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/KRuK-Y9uEP2Hwat5ojD1E.jpeg)
# Results
![results.png](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/-DOb5ZmGoI_vXs7zgtFMP.png)
# Predict
![train_batch0.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/Ie7m1EQosL87TbN_UoS-0.jpeg)
![train_batch1.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/Lr3solcPWqHrdMvQ0hBX9.jpeg)
```
YOLOv10s summary (fused): 293 layers, 8,038,056 parameters, 0 gradients, 24.5 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|ββββββββββ| 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
```
# Others models Counter Strike 2 YOLOv10m Object Detection
https://huggingface.co/ChitoParedes/cs2-yolov10m |