---
license: cc-by-nc-nd-4.0
pipeline_tag: object-detection
tags:
- yolov9
- ultralytics
- yolo
- object-detection
- pytorch
- cs2
- Counter Strike
---

Counter Strike 2 players detector

## Supported Labels

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

## All models

- [yoloV8n_cs2](https://huggingface.co/Vombit/yolov8n_cs2) (6mb)
- [yoloV8s_cs2](https://huggingface.co/Vombit/yolov8s_cs2) (21mb)
- [yoloV9c_cs2](https://huggingface.co/Vombit/yolov9c_cs2) (50mb)

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

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

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


## Predict info

Ultralytics YOLOv8.2.3 🚀 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (NVIDIA GeForce RTX 4060, 8187MiB):

- yolov9c_cs2_fp16.engine (640x640 5 ts, 5 ths, 5.0ms)
- yolov9c_cs2.engine (640x640 5 ts, 5 ths, 15.1ms)
- yolov9c_cs2.onnx (640x640 5 ts, 5 ths, 27.5ms)
- yolov9c_cs2.pt (384x640 5 ts, 5 ths, 272.8ms)


## Dataset info

Data from over 70 games, where the footage has been tagged in detail.
<img width="640" src="https://huggingface.co/Vombit/yolov9c_cs2/resolve/main/labels.jpg">


## Train info

The training took place over 100 epochs.
<img width="640" src="https://huggingface.co/Vombit/yolov9c_cs2/resolve/main/results.png">



You can also support me with a cup of coffee: [donate](https://www.donationalerts.com/r/vombit_donation)