---
license: cc-by-nc-nd-4.0
pipeline_tag: object-detection
tags:
- yolo11
- ultralytics
- yolo
- object-detection
- pytorch
- cs2
- Counter Strike
---

Counter Strike 2 players detector

## Supported Labels

```
[ 'c', 'ch', 't', 'th' ]
```

## All models in this series

- [yolo11n_cs2](https://huggingface.co/Vombit/yolo11n_cs2) (~6mb)
- [yolo11s_cs2](https://huggingface.co/Vombit/yolo11s_cs2) (~18mb)
- [yolo11m_cs2](https://huggingface.co/Vombit/yolo11m_cs2) (~39mb)
- [yolo11l_cs2](https://huggingface.co/Vombit/yolo11l_cs2) (~49mb)
- [yolo11x_cs2](https://huggingface.co/Vombit/yolo11x_cs2) (~109mb)

## How to use
```python
# load Yolo
from ultralytics import YOLO

# Load a pretrained YOLO model
model = YOLO(r'weights\yolo**_cs2.pt')

# Run inference on 'image.png' with arguments
model.predict(
    'image.png',
    save=True,
    device=0
    )
```


## Predict info

Ultralytics 8.3.68 🚀 Python-3.11.0 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4060, 8187MiB)

- yolo11n_cs2_fp16.engine (384x640 5 ts, 5 ths, 20.2ms)
- yolo11n_cs2.engine (384x640 5 ts, 5 ths, 3.3ms)
- yolo11n_cs2_fp16.onnx (640x640 5 ts, 5 ths, 7.8ms)
- yolo11n_cs2.onnx (384x640 5 ts, 5 ths, 172.7ms)
- yolo11n_cs2.pt (384x640 5 ts, 5 ths, 52.1ms)

## Dataset info

Data from over 127 games, where the footage has been tagged in detail.

![image/jpg](https://huggingface.co/Vombit/yolo11n_cs2/resolve/main/labels.jpg)
![image/jpg](https://huggingface.co/Vombit/yolo11n_cs2/resolve/main/labels_correlogram.jpg)


## Train info

The training took place over 150 epochs.

![image/png](https://huggingface.co/Vombit/yolo11n_cs2/resolve/main/results.png)




You can also support me with a cup of coffee: [donate](http://185.105.118.103/donation)