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---
license: cc-by-nc-nd-4.0
pipeline_tag: object-detection
tags:
- yolov10
- ultralytics
- yolo
- object-detection
- pytorch
- cs2
- Counter Strike
---
Counter Strike 2 players detector
## Supported Labels
```
[ 'c', 'ch', 't', 'th' ]
```
## All models in this series
- [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb)
- [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb)
- [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb)
- [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb)
- [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb)
- [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb)
## 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 YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB)
- yolov10n_cs2_fp16.engine (640x640 5 ts, 5 ths, 2.6ms)
- yolov10n_cs2.engine (640x640 5 ts, 5 ths, 2.9ms)
- yolov10n_cs2_fp16.onnx (640x640 5 ts, 5 ths, 32.6ms)
- yolov10n_cs2.onnx (640x640 5 ts, 5 ths, 40.6ms)
- yolov10n_cs2.pt (384x640 5 ts, 5 ths, 124.3ms)
## Dataset info
Data from over 120 games, where the footage has been tagged in detail.
## Train info
The training took place over 150 epochs.
You can also support me with a cup of coffee: [donate](https://www.donationalerts.com/r/vombit_donation) |